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Levels of evidence in research

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Level of evidence hierarchy

When carrying out a project you might have noticed that while searching for information, there seems to be different levels of credibility given to different types of scientific results. For example, it is not the same to use a systematic review or an expert opinion as a basis for an argument. It’s almost common sense that the first will demonstrate more accurate results than the latter, which ultimately derives from a personal opinion.

In the medical and health care area, for example, it is very important that professionals not only have access to information but also have instruments to determine which evidence is stronger and more trustworthy, building up the confidence to diagnose and treat their patients.

5 levels of evidence

With the increasing need from physicians – as well as scientists of different fields of study-, to know from which kind of research they can expect the best clinical evidence, experts decided to rank this evidence to help them identify the best sources of information to answer their questions. The criteria for ranking evidence is based on the design, methodology, validity and applicability of the different types of studies. The outcome is called “levels of evidence” or “levels of evidence hierarchy”. By organizing a well-defined hierarchy of evidence, academia experts were aiming to help scientists feel confident in using findings from high-ranked evidence in their own work or practice. For Physicians, whose daily activity depends on available clinical evidence to support decision-making, this really helps them to know which evidence to trust the most.

So, by now you know that research can be graded according to the evidential strength determined by different study designs. But how many grades are there? Which evidence should be high-ranked and low-ranked?

There are five levels of evidence in the hierarchy of evidence – being 1 (or in some cases A) for strong and high-quality evidence and 5 (or E) for evidence with effectiveness not established, as you can see in the pyramidal scheme below:

Level 1: (higher quality of evidence) – High-quality randomized trial or prospective study; testing of previously developed diagnostic criteria on consecutive patients; sensible costs and alternatives; values obtained from many studies with multiway sensitivity analyses; systematic review of Level I RCTs and Level I studies.

Level 2: Lesser quality RCT; prospective comparative study; retrospective study; untreated controls from an RCT; lesser quality prospective study; development of diagnostic criteria on consecutive patients; sensible costs and alternatives; values obtained from limited stud- ies; with multiway sensitivity analyses; systematic review of Level II studies or Level I studies with inconsistent results.

Level 3: Case-control study (therapeutic and prognostic studies); retrospective comparative study; study of nonconsecutive patients without consistently applied reference “gold” standard; analyses based on limited alternatives and costs and poor estimates; systematic review of Level III studies.

Level 4: Case series; case-control study (diagnostic studies); poor reference standard; analyses with no sensitivity analyses.

Level 5: (lower quality of evidence) – Expert opinion.

By looking at the pyramid, you can roughly distinguish what type of research gives you the highest quality of evidence and which gives you the lowest. Basically, level 1 and level 2 are filtered information – that means an author has gathered evidence from well-designed studies, with credible results, and has produced findings and conclusions appraised by renowned experts, who consider them valid and strong enough to serve researchers and scientists. Levels 3, 4 and 5 include evidence coming from unfiltered information. Because this evidence hasn’t been appraised by experts, it might be questionable, but not necessarily false or wrong.

Examples of levels of evidence

As you move up the pyramid, you will surely find higher-quality evidence. However, you will notice there is also less research available. So, if there are no resources for you available at the top, you may have to start moving down in order to find the answers you are looking for.

• Systematic Reviews: -Exhaustive summaries of all the existent literature about a certain topic. When drafting a systematic review, authors are expected to deliver a critical assessment and evaluation of all this literature rather than a simple list. Researchers that produce systematic reviews have their own criteria to locate, assemble and evaluate a body of literature.
• Meta-Analysis: Uses quantitative methods to synthesize a combination of results from independent studies. Normally, they function as an overview of clinical trials. Read more: Systematic review vs meta-analysis .
• Critically Appraised Topic: Evaluation of several research studies.
• Critically Appraised Article: Evaluation of individual research studies.
• Randomized Controlled Trial: a clinical trial in which participants or subjects (people that agree to participate in the trial) are randomly divided into groups. Placebo (control) is given to one of the groups whereas the other is treated with medication. This kind of research is key to learning about a treatment’s effectiveness.
• Cohort studies: A longitudinal study design, in which one or more samples called cohorts (individuals sharing a defining characteristic, like a disease) are exposed to an event and monitored prospectively and evaluated in predefined time intervals. They are commonly used to correlate diseases with risk factors and health outcomes.
• Case-Control Study: Selects patients with an outcome of interest (cases) and looks for an exposure factor of interest.
• Background Information/Expert Opinion: Information you can find in encyclopedias, textbooks and handbooks. This kind of evidence just serves as a good foundation for further research – or clinical practice – for it is usually too generalized.

Of course, it is recommended to use level A and/or 1 evidence for more accurate results but that doesn’t mean that all other study designs are unhelpful or useless. It all depends on your research question. Focusing once more on the healthcare and medical field, see how different study designs fit into particular questions, that are not necessarily located at the tip of the pyramid:

• Questions concerning therapy: “Which is the most efficient treatment for my patient?” >> RCT | Cohort studies | Case-Control | Case Studies
• Questions concerning diagnosis: “Which diagnose method should I use?” >> Prospective blind comparison
• Questions concerning prognosis: “How will the patient’s disease will develop over time?” >> Cohort Studies | Case Studies
• Questions concerning etiology: “What are the causes for this disease?” >> RCT | Cohort Studies | Case Studies
• Questions concerning costs: “What is the most cost-effective but safe option for my patient?” >> Economic evaluation
• Questions concerning meaning/quality of life: “What’s the quality of life of my patient going to be like?” >> Qualitative study

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Levels of Evidence and Study Design: Levels of Evidence

Levels of evidence.

• Study Design
• Study Design by Question Type
• Rating Systems

This is a general set of levels to aid in critically evaluating evidence. It was adapted from the model presented in the book, Evidence-Based Practice in Nursing and Healthcare: A Guide to Best Practice  (Melnyk & Fineout-Overholt, 2019). Some specialties may have adopted a slightly different and/or smaller set of levels.

Evidence from a clinical practice guideline based on systematic reviews or meta-analyses of randomized controlled trials. Is this is not available, then evidence from a systematic review or meta-analysis of random controlled trials.

Evidence from randomized controlled studies with good design.

Evidence from controlled trials that have good design but are not randomized.

Evidence from case-control and cohort studies with good design.

Evidence from systematic reviews of qualitative and descriptive studies.

Evidence from qualitative and descriptive studies.

Evidence from the opinion of authorities and/or the reports of expert committees. 

Evidence Pyramid

The pyramid below is a hierarchy of evidence for quantitative studies. It shows the hierarchy of studies by study design; starting with secondary and reappraised studies, then primary studies, and finally reports and opinions, which have no study design. This pyramid is a simplified, amalgamation of information presented in the book chapter “Evidence-based decision making” (Forest et al., 2019) and book Evidence-Based Practice in Nursing and Healthcare: A Guide to Best Practice  (Melnyk & Fineout-Overholt, 2019).

Evidence Table for Nursing

Advocate Health - Midwest provides system-wide evidence based practice resources. The Nursing Hub* has an Evidence-Based Quality Improvement (EBQI) Evidence Table , within the Evidence-Based Practice (EBP) Resource. It also includes information on evidence type, and a literature synthesis table.

*The Nursing Hub requires access to the Advocate Health - Midwest SharePoint platform.

Forrest, J. L., Miller, S. A., Miller, G. W., Elangovan, S., & Newman, M. G. (2019). Evidence-based decision making. In M. G. Newman, H. H. Takei, P. R. Klokkevold, & F. A. Carranza (Eds.),  Newman and Carranza's clinical periodontology  (13th ed., pp. 1-9.e1). Elsevier.

• Melnyk, B. M., & Fineout-Overholt, E. (2019).  Evidence-based practice in nursing and healthcare: A guide to best practice  (4th ed.). Wolters Kluwer. 
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Systematic Reviews

• Levels of Evidence
• Evidence Pyramid
• Joanna Briggs Institute

The evidence pyramid is often used to illustrate the development of evidence. At the base of the pyramid is animal research and laboratory studies – this is where ideas are first developed. As you progress up the pyramid the amount of information available decreases in volume, but increases in relevance to the clinical setting.

Meta Analysis  – systematic review that uses quantitative methods to synthesize and summarize the results.

Systematic Review  – summary of the medical literature that uses explicit methods to perform a comprehensive literature search and critical appraisal of individual studies and that uses appropriate st atistical techniques to combine these valid studies.

Randomized Controlled Trial – Participants are randomly allocated into an experimental group or a control group and followed over time for the variables/outcomes of interest.

Cohort Study – Involves identification of two groups (cohorts) of patients, one which received the exposure of interest, and one which did not, and following these cohorts forward for the outcome of interest.

Case Control Study – study which involves identifying patients who have the outcome of interest (cases) and patients without the same outcome (controls), and looking back to see if they had the exposure of interest.

Case Series   – report on a series of patients with an outcome of interest. No control group is involved.

• Levels of Evidence from The Centre for Evidence-Based Medicine
• The JBI Model of Evidence Based Healthcare
• How to Use the Evidence: Assessment and Application of Scientific Evidence From the National Health and Medical Research Council (NHMRC) of Australia. Book must be downloaded; not available to read online.

When searching for evidence to answer clinical questions, aim to identify the highest level of available evidence. Evidence hierarchies can help you strategically identify which resources to use for finding evidence, as well as which search results are most likely to be "best".                                             

Image source: Evidence-Based Practice: Study Design from Duke University Medical Center Library & Archives. This work is licensed under a Creativ e Commons Attribution-ShareAlike 4.0 International License .

The hierarchy of evidence (also known as the evidence-based pyramid) is depicted as a triangular representation of the levels of evidence with the strongest evidence at the top which progresses down through evidence with decreasing strength. At the top of the pyramid are research syntheses, such as Meta-Analyses and Systematic Reviews, the strongest forms of evidence. Below research syntheses are primary research studies progressing from experimental studies, such as Randomized Controlled Trials, to observational studies, such as Cohort Studies, Case-Control Studies, Cross-Sectional Studies, Case Series, and Case Reports. Non-Human Animal Studies and Laboratory Studies occupy the lowest level of evidence at the base of the pyramid.

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• Getting Started
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Understanding Evidence Levels in Evidence-Based Medicine: A Guide for Healthcare Professionals

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Critically-appraised individual articles and synopses include:

Filtered evidence:

• Level I: Evidence from a systematic review of all relevant randomized controlled trials.
• Level II: Evidence from a meta-analysis of all relevant randomized controlled trials.
• Level III: Evidence from evidence summaries developed from systematic reviews
• Level IV: Evidence from guidelines developed from systematic reviews
• Level V: Evidence from meta-syntheses of a group of descriptive or qualitative studies
• Level VI: Evidence from evidence summaries of individual studies
• Level VII: Evidence from one properly designed randomized controlled trial

Unfiltered evidence:

• Level VIII: Evidence from nonrandomized controlled clinical trials, nonrandomized clinical trials, cohort studies, case series, case reports, and individual qualitative studies.
• Level IX: Evidence from opinion of authorities and/or reports of expert committee

Two things to remember:

1. Studies in which randomization occurs represent a higher level of evidence than those in which subject selection is not random.

2. Controlled studies carry a higher level of evidence than those in which control groups are not used.

Strength of Recommendation Taxonomy (SORT)

• SORT The American Academy of Family Physicians uses the Strength of Recommendation Taxonomy (SORT) to label key recommendations in clinical review articles. In general, only key recommendations are given a Strength-of-Recommendation grade. Grades are assigned on the basis of the quality and consistency of available evidence.
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Hierarchy of Scientific Evidence: Understanding the Levels

This article provides an in-depth exploration of the hierarchy of scientific evidence , emphasising its significance in research interpretation and application.

It examines different hierarchical levels, highlights the contributions of Guyatt and Sackett, and discusses the GRADE approach.

It also compares various grading systems and guidelines used in assessing evidence-based practices.

This knowledge is critical for researchers, policy makers, and students in understanding and effectively utilising scientific evidence.

Key Takeaways

• Levels of evidence are a hierarchical system for classifying evidence in Evidence-Based Medicine (EBM).
• Randomised controlled trials (RCTs) are ranked at the highest level of evidence due to their unbiased design.
• Case series or expert opinions are ranked at the lowest level of evidence due to potential bias.
• The GRADE approach rates the quality of evidence as high, moderate, low, or very low.

Exploring the Concept of Hierarchy of Evidence

Examining the concept of hierarchy of evidence helps in understanding the varying degrees of reliability and validity of scientific studies, ranging from randomised controlled trials at the top to expert opinions at the bottom.

The hierarchy of scientific evidence is a system used to rank the strength of research findings. At the top are systematic reviews and meta-analyses of randomised controlled trials (RCTs) which provide the most reliable evidence. RCTs are followed in the hierarchy by well-designed non-randomised controlled studies.

Lower levels of scientific evidence include observational studies and case series. The lowest level of evidence is derived from expert opinions and anecdotal evidence.

Understanding this hierarchy is crucial in assessing the credibility and applicability of research in clinical decision-making.

Understanding the GRADE Approach

The GRADE Approach, an advanced tool for evaluating the quality of evidence in healthcare, prioritises transparency and strives for simplicity, making it a fundamental asset in the realm of evidence-based medicine. This approach, endorsed by over 100 organisations globally, provides a systematic and transparent method of appraising evidence.

• Quality of Evidence Assessment : GRADE classifies evidence quality into four levels: high, moderate, low, and very low. The higher the quality, the more we can trust the evidence.
• Strength of Recommendations : GRADE distinguishes between strong and weak recommendations, bringing clarity to decision-making processes.
• Consideration of Values and Preferences : GRADE acknowledges the role of patient values and preferences, ensuring a patient-centric approach to healthcare. This multifaceted approach enhances the credibility and applicability of research findings in clinical practice.

An Insight Into the Guyatt and Sackett Hierarchy

Both Guyatt and Sackett introduced a seminal hierarchy of evidence in 1995, which placed systematic reviews and meta-analyses of randomised controlled trials at the pinnacle, and relegated case reports to the bottom, thereby revolutionising the approach to evidence-based medicine.

This groundbreaking work stressed the importance of rigorous scientific methodology in medical research and practice. The hierarchy underscores the need for high-quality experimental designs to yield reliable evidence.

The emphasis on systematic reviews and meta-analyses highlights the value of consolidating data from multiple studies. Conversely, the low rank of case reports reflects their inherent limitations, including potential bias and lack of generalizability.

This hierarchy has profoundly shaped the field of evidence-based medicine, promoting the use of robust, high-quality evidence in clinical decision-making.

Comparing the Saunders Et Al. Protocol and Khan Et Al. Protocol

Diving into a comparative analysis, we find that the Saunders et al. protocol bases its categorisation of interventions on research design, potential harm, and general acceptance, while the Khan et al. protocol puts a stronger emphasis on the use of randomised designs and intention-to-treat analysis.

Three distinct differences emerge:

• Emphasis on Research Design: The Saunders protocol accommodates a wide range of research designs, while Khan’s protocol prefers randomised designs, thus narrowing its range of acceptable studies.
• Consideration of Potential Harm: Saunders et al. consider potential harm in their categorisation, a factor not prominently accounted for in Khan et al.’s protocol.
• Acceptance in the Scientific Community: Saunders et al. consider general acceptance among scientists, feeding into a more holistic approach. Conversely, Khan et al. focus primarily on rigorous, statistically backed evidence.

A Look at Different Grading Systems and Guidelines

Assessing different grading systems and guidelines is crucial for understanding their efficacy in evaluating research quality, and it allows for a comparative analysis between approaches such as GRADEpro, GRADE guidelines, and BMJ Best Practice.

GRADEpro and GRADE guidelines, collectively known as GRADE, provide a systematic and transparent means of appraising evidence, widely recognised and endorsed by over 100 health organisations worldwide.

Meanwhile, BMJ Best Practice offers a comprehensive solution integrating the latest research evidence, guidelines, and expert opinion. It facilitates evidence-based decisions in clinical practice.

However, each system has its strengths and weaknesses, making the choice largely dependent on the context and specific requirements of each research project.

A thorough understanding of these grading systems is essential to their effective utilisation.

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Frequently asked questions, how does the hierarchy of scientific evidence affect scientific content.

The hierarchy of scientific evidence significantly influences scientific content by determining the credibility and weight of the information presented. High-quality content often relies on sources from the upper levels of the hierarchy, such as systematic reviews and randomized controlled trials, which are considered more reliable due to their rigorous methodology and comprehensive analysis. This hierarchy guides researchers, publishers, and decision-makers in evaluating the strength of evidence behind scientific claims, ensuring that the most robust, well-supported findings are highlighted and disseminated. Consequently, it shapes the development of scientific theories, the formulation of guidelines, and the direction of future research, prioritizing evidence-based knowledge and practices in the scientific community.

How Are Different Types of Studies Weighted in the Hierarchy of Scientific Evidence?

In the hierarchy of scientific evidence, different types of studies are weighted based on their design and validity. Randomised controlled trials are typically at the top, while observational studies and expert opinions rank lower.

How Does the GRADE Approach Differ From Other Methods of Evaluating Scientific Evidence?

The GRADE approach to evaluating scientific evidence differs from other methods by offering a systematic and transparent process. It assesses the quality of evidence and strength of recommendation, widely endorsed by over 100 organisations globally.

What Are the Key Components of the Guyatt and Sackett Hierarchy?

The Guyatt and Sackett hierarchy is a system that categorises scientific evidence by its strength. It places systematic reviews and meta-analyses of randomised controlled trials at the top, followed by lesser reliability evidence such as case reports.

How Do the Saunders Et Al. Protocol and the Khan Et Al. Protocol Compare in Terms of Research Design and Analysis?

The Saunders et al. protocol categorises interventions based on research design, potential harm, and general acceptance. In contrast, the Khan et al. protocol emphasises randomised designs and intention-to-treat analysis for a rigorous approach.

Can You Provide Examples of Different Grading Systems and Guidelines Used in Evidence-Based Practices and Medicine?

Examples of grading systems used in evidence-based medicine include GRADEpro, GRADE guidelines, BMJ Best Practice, and User’s guides to the medical literature. The NREPP Review Criteria assesses evidence-based practices and programs.

How Does the Hierarchy of Scientific Evidence Relate to Life Science Content?

In the realm of life sciences content , the hierarchy of scientific evidence ensures that content, especially impacting health and medical practices, is grounded in the most reliable studies. Systematic reviews and randomized controlled trials are prioritized for their thorough analysis and reduced bias, influencing publications, clinical guidelines, and policy-making. This ensures life science content is accurate, rigorously tested, and supports informed healthcare decisions.

In conclusion, understanding the hierarchy of scientific evidence is crucial for interpreting and applying research outcomes. This knowledge enhances the interpretation of methodological quality, results consistency, and subject relevance.

The contributions of Guyatt, Sackett, and the GRADE approach significantly influence this field. Grading systems and guidelines further aid in assessing evidence-based practices, underscoring the indispensability of understanding the levels of scientific evidence for researchers, policy makers, and students alike.

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Evidence-Based Research: Evidence Types

Introduction.

Not all evidence is the same, and appraising the quality of the evidence is part of evidence-based practice research. The hierarchy of evidence is typically represented as a pyramid shape, with the smaller, weaker and more abundant research studies near the base of the pyramid, and systematic reviews and meta-analyses at the top with higher validity but a more limited range of topics.

Several versions of the evidence pyramid have evolved with different interpretations, but they are all comprised of the types of evidence discussed on this page. Walden's Nursing 6052 Essentials of Evidence-Based Practice class currently uses a simplified adaptation of the Johns Hopkins model .

Evidence Levels:

Level I:  Experimental, randomized controlled trial (RCT), systematic review RTCs with or without meta-analysis

Level II:  Quasi-experimental studies, systematic review of a combination of RCTs and quasi-experimental studies, or quasi-experimental studies only, with or without meta-analysis

Level III:  Nonexperimental, systematic review of RCTs, quasi-experimental with/without meta-analysis, qualitative, qualitative systematic review with/without meta-synthesis  (see Daly 2007 for a sample qualitative hierarchy) 

Level IV : Respected authorities’ opinions, nationally recognized expert committee or consensus panel reports based on scientific evidence

Level V:  Literature reviews, quality improvement, program evaluation, financial evaluation, case reports, nationally recognized expert(s) opinion based on experiential evidence

Systematic review

What is a systematic review.

A systematic review is a type of publication that addresses a clinical question by analyzing research that fits certain explicitly-specified criteria. The criteria for inclusion is usually based on research from clinical trials and observational studies. Assessments are done based on stringent guidelines, and the reviews are regularly updated. These are usually considered one of the highest levels of evidence and usually address diagnosis and treatment questions.

Benefits of Systematic Reviews

Systematic reviews refine and reduce large amounts of data and information into one document, effectively summarizing the evidence to support clinical decisions. Since they are typically undertaken by a entire team of experts, they can take months or even years to complete, and must be regularly updated. The teams are usually comprised of content experts, an experienced searcher, a bio-statistician, and a methodologist. The team develops a rigorous protocol to thoroughly locate, identify, extract, and analyze all of the evidence available that addresses their specific clinical question.

As systematic reviews become more frequently published, concern over quality led to the PRISMA Statement to establish a minimum set of items for reporting in systematic reviews and meta-analyses.

Many systematic reviews also contain a meta-analysis.

What is a Meta-Analysis?

Meta-analysis is a particular type of systematic review that focuses on selecting and reviewing quantitative research. Researchers conducting a meta-analysis combine the results of several independent studies and reviews to produce a synthesis where possible. These publications aim to assist in making decisions about a particular therapy.

Benefits of Meta-Analysis

A meta-analysis synthesizes large amounts of data using a statistical examination. This type of analysis provides for some control between studies and generalized application to the population.

To learn how to find systematic reviews in the Walden Library, please see the Levels of Evidence Pyramid page:

• Levels of Evidence Pyramid: Systematic Reviews

Further reading

• Cochrane Handbook for Systematic Reviews of Interventions *updated 2022

Guidelines & summaries

Practice guidelines.

A practice guideline is a systematically-developed statement addressing common patient health care decisions in specific clinical settings and circumstances.  They should be valid, reliable, reproducible, clinically applicable, clear and flexible. Documentation must be included and referenced. Practice guidelines may come from organizations, associations, government entities, and hospitals/health systems.

ECRI Guidelines Trust

Best Evidence Topics

Best evidence topics are sometimes referred to as Best BETs. These topics are developed and supported for situations or setting when the high levels of evidence don't fit or are unavailable. They originated from emergency medicine providers' need to conduct rapid evidence-based clinical decisions.

Critically-Appraised Topics

Critically-appraised topics are a standardized one- to two-page summary of the evidence supporting a clinical question. They include a critique of the literature and statement of relevant results. They can be found online in many repositories.

To learn how to find critically-appraised topics in the Walden Library, please see the Levels of Evidence Pyramid page:

• Levels of Evidence Pyramid: Critically-Appraised Topics

Critically-Appraised Articles

Critically-appraised articles are individual articles by authors that evaluate and synopsize individual research studies. ACP Journal Club is the most well known grouping of titles that include critically appraised articles.

To learn how to find critically-appraised articles in the Walden Library, please see the Levels of Evidence Pyramid page:

• Levels of Evidence Pyramid: Critically-Appraised Articles

Randomized controlled trial

A randomized controlled trial (RCT) is a clinical trial in which participants are randomly assigned to either the treatment group or control group. This random allocation of participants helps to reduce any possible selection bias and makes the RCT a high level of evidence. Having a control group, which receives no treatment or a placebo treatment, to compare the treatment group against allows researchers to observe the potential efficacy of the treatment when other factors remain the same. Randomized controlled trials are quantitative studies and are often the only studies included in systematic reviews.

To learn how to find randomize controlled trials, please see our CINAHL & MEDLINE help pages:

• CINAHL Search Help: Randomized Controlled Trials
• MEDLINE Search Help: Randomized Controlled Trials

Cohort study

A cohort study is an observational longitudinal study that analyzes risk factors and outcomes by following a group (cohort) that share a common characteristic or experience over a period of time.

Cohort studies can be retrospective, looking back over time at data that has already been collected, or can be prospective, following a group forward into the future and collecting data along the way.

While cohort studies are considered a lower level of evidence than randomized controlled trials, they may be the only way to study certain factors ethically. For example, researchers may follow a cohort of people who are tobacco smokers and compare them to a cohort of non-smokers looking for outcomes. That would be an ethical study. It would be highly unethical, however, to design a randomized controlled trial in which one group of participants are forced to smoke in order to compare outcomes.

To learn how to find cohort studies, please see our CINAHL and MEDLINE help pages:

• CINAHL Search Help: Cohort Studies
• MEDLINE Search Help: Cohort Studies

Case-controlled studies

Case-controlled studies are a type of observational study that looks at patients who have the same disease or outcome. The cases are those who have the disease or outcome while the controls do not. This type of study evaluates the relationship between diseases and exposures by retrospectively looking back to investigate what could potentially cause the disease or outcome.

To learn how to find case-controlled studies, please see our CINAHL and MEDLINE help pages:

• CINAHL Search Help: Case Studies
• MEDLINE Search Help: Case Studies

Background information & expert opinion

Background information and expert opinion can be found in textbooks or medical books that provide basic information on a topic. They can be helpful to make sure you understand a topic and are familiar with terms associated with it.

To learn about accessing background information, please see the Levels of Evidence Pyramid page:

• Levels of Evidence Pyramid: Background Information & Expert Opinion
• Previous Page: Levels of Evidence Pyramid
• Next Page: CINAHL Search Help
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Levels of Evidence

Levels of evidence (or hierarchy of evidence) is a system used to rank medical studies based on the quality and reliability of their designs. The levels of evidence are commonly depicted in a pyramid model that illustrates both the quality and quantity of available evidence. The higher the position on the pyramid, the stronger the evidence. 1 Each level builds on data and research previously developed in the lower tiers.

Levels of evidence pyramids are often divided into two or three sections. The top section consists of filtered (secondary) evidence, including systematic reviews, meta-analyses, and critical appraisals. The section below includes unfiltered (primary) evidence, including randomized controlled trials, cohort studies, case-controlled studies, case series, and case reports. 1 Some models include an additional bottom segment for background information and expert opinion. 2

Definitions

Systematic Review and Meta-Analysis

A systematic review synthesizes the results from available studies of a particular health topic, answering a specific research question by collecting and evaluating all research evidence that fits the reviewer’s selection criteria. 3 The most well-known collection of systematic reviews is the Cochrane Database of Systematic Reviews .

Systematic reviews can include meta-analyses in which statistical methods are applied to evaluate and synthesize quantitative results from multiple studies.

A randomized controlled trial is a prospective study that measures the efficacy of an intervention or treatment. Subjects are randomly assigned to either an experimental group or a control group; the control group receives a placebo or sham intervention, while the experimental group receives the intervention being studied. Randomizing subjects is effective at removing bias, thus increasing the validity of the research. RCTs are frequently blinded so that neither the subjects (single blind), nor the clinicians (double blind), nor the researchers (triple blind) know in which group the subjects are placed. 4

A cohort study is a type of observational study, meaning that no intervention is taken among the subjects. It is also a type of longitudinal study in which research subjects are followed over a period of time. 5 A cohort study can be either prospective, which collects new data over time, or retrospective, which uses previously acquired data or medical records. This type of study examines a group of people who share a common trait or exposure and are assessed based on whether they develop an outcome of interest. An example of a prospective cohort study is a study that determines which subjects smoke and then many years later assesses the incidence of lung cancer in both smokers and non-smokers.

A case-control study is another type of observational study. It is also a type of retrospective study that looks back in time to assess information. A case-control study compares people who have the specified condition or outcome being studied (known as “cases”) with people who do not have the condition or outcome (known as “controls”). 6 An example of a case-control study is a study that assesses the lifetime smoking exposure of patients with and without lung cancer.

A case report is a detailed report of the presentation, diagnosis, treatment, treatment response, and follow-up after treatment of an individual patient. A case series is a group of case reports involving patients who share similar characteristics. A case series is observational and can be conducted either retrospectively or prospectively.

Also called a prevalence study, a cross-sectional study examines subjects at a single point in time. By definition, a cross-sectional study is only observational. 7 An example of a cross-sectional study is a survey of a population to determine the prevalence of lung cancer.

Filtered vs. Unfiltered Information

Filtered (secondary) levels of evidence include information that has been previously collected, analyzed, and aggregated by expert analysis and review. Filtered levels of evidence are placed above unfiltered levels of evidence on the pyramid. Examples of filtered levels of evidence are systematic reviews and meta-analyses.

Unfiltered (primary) evidence includes original research studies, including randomized controlled trials and case-control studies. They are often published in peer-reviewed journals. 8 However, these studies have not been subjected to additional analysis and review beyond that of the peer reviewers for each study. In most cases, unfiltered levels of evidence are difficult to apply in clinical decision-making. 9

In 1972, Archibald Cochrane, a physician from Scotland, wrote Effectiveness and Efficiency, in which he argued that decisions about medical treatment should be based on a systematic review of the available clinical evidence. Cochrane proposed an international collaboration of researchers to systematically review the best clinical studies in each specialty. 10

In 1979, the Canadian Task Force on the Periodic Health Examination published a ranking system for medical evidence, proposing four quality levels: 11,12

• I: Evidence obtained from at least one properly designed randomized controlled trial
• II-1: Evidence obtained from a well-designed cohort or case-control analytic study, preferably from more than one center or research group
• II-2: Evidence obtained from comparisons between times or places with or without the intervention
• III: Opinions of respected authorities, based on clinical experience, descriptive studies, or reports of expert committees

The U.S. Preventive Services Task Force (USPSTF) adopted a modified version of the Canadian Task Force’s categorization in 1988: 13,14

• II-1: Evidence obtained from well-designed controlled trials without randomization
• II-2: Evidence obtained from well-designed cohort or case-control analytic studies, preferably from more than one center or research group
• II-3: Evidence obtained from multiple time series designs with or without the intervention; dramatic results in uncontrolled trials might also be regarded as this type of evidence

The physician Gordon Guyatt, who in 1991 coined the term “evidence-based medicine,” proposed another approach to classifying the strength of recommendations in Users' Guides to the Medical Literature . 15, 16 Referencing Guyatt’s paper, Trisha Greenhalgh summarized his revised hierarchy as follows: 17

• Systematic reviews and meta-analyses
• Randomized controlled trials with definitive results (confidence intervals that do not overlap the threshold of a clinically significant effect)
• Randomized controlled trials with non-definitive results (a point estimate that suggests a clinically significant effect but with confidence intervals overlapping the threshold for this effect)
• Cohort studies
• Case-control studies
• Cross-sectional surveys
• Case reports

Evidence levels can vary based on the clinical question being asked (i.e., the categorization of evidence for a medical treatment may differ from evidence for determining disease prevalence). For example, The Centre for Evidence-Based Medicine and American Society of Plastic Surgeons published tables specific to therapeutic, diagnostic, and prognostic studies. 18,19

• Murad MH, Asi N, Alsawas M, Alahdab F. New evidence pyramid. BMJ Evidence Based Medicine. 2016;21(4):125–127.
• Illustration adapted from model displayed in “Evidence-Based Practice in Health”. The model is attributed to the National Health and Medical Research Council. NHMRC levels of evidence and grades for recommendations for developers of guidelines. Retrieved from University of Canberra Library.
• Turner M. “Evidence-Based Practice in Health”. 2014. Retrieved from University of Canberra website.
• Hariton E, Locascio JJ. Randomised controlled trials—The gold standard for effectiveness research: Study design: Randomised controlled trials. BJOG. 2018;125(13):1716.
• Barrett D, Noble H. What are cohort studies? Evid Based Nur. 2019;22(4):95–6.
• Himmelfarb Health Sciences Library. Study design 101: Case control study. 2019.
• Singh Setia M. Methodology Series Module 3: Cross-sectional Studies. Indian J Dermatol. 2016;61(3):261–264.
• Northern Virginia Community College. Evidence-based practice for health professionals. 2022.
• Kendall S. Evidence-based resources simplified. Can Fam Physician. 2008;54(2):241–243.
• Stavrou A, Challoumas D, Dimitrakakis G. Archibald Cochrane (1909–1988): The father of evidence-based medicine. Interact Cardiovasc Thorac Surg. 2014;18(1):121–124.
• Spitzer WO, et al. The periodic health examination. Canadian Task Force on the Periodic Health Examination. Can Med Assoc J. 1979;121(9):1193–1254.
• Burns PB, Rohrich RJ, Chung KC. The Levels of Evidence and their Role in Evidence-Based Medicine. Plastic and Reconstructive Surgery. 2010:128(1):305–310.
• U.S. Preventive Services Task Force. (as of 2018). Grade definitions.
• U.S. Preventive Services Task Force. Guide to Clinical Preventive Services: Report of the U.S. Preventive Services Task Force. DIANE Publishing, 1989. ISBN 1568062974.
• Guyatt GH, Sackett DL, Sinclair JC, Hayward R, Cook DJ, Cook RJ. Users’ guides to the medical literature IX. A method for grading health care recommendations. Evidence-Based Medicine Working Group. JAMA. 1995;274(22):1800–1804.
• Zimerman AL. Evidence-Based Medicine: A Short History of a Modern Medical Movement. Virtual Mentor. 2013;15(1):71–76.
• Greenhalgh T. How to read a paper. Getting your bearings (deciding what the paper is about). BMJ. 1997;315(7102):243–246. doi:10.1136/bmj.315.7102.243
• Sullivan D, Chung KC, Eaves FF 3rd, Rohrich RJ. The level of evidence pyramid: Indicating levels of evidence in Plastic and Reconstructive Surgery articles. Plast Reconstr Surg. 2011;128(1):311–314. doi:10.1097/PRS.0b013e3182195826
• Oxford Centre for Evidence-Based Medicine: Levels of evidence. March 2009. CEBM.

Contributors

• Moira Tannenbaum, MSN
• Stacy Sebastian, MD
• Brian Sullivan, MD

Published: August 17, 2021

Updated: November 1, 2022

How Do I...?

• How Do I...? Home
• ...Use the library (in general)?
• ...Select a Topic and Make a Research Plan?
• ...Determine the Types and Levels of Sources?
• ...Evaluate Information?
• ...Find Information in the Library Collection?
• ...Use Sources Ethically and Create Correct Citations
• ...Get More Help?

Types and Levels of Sources

The types of sources you use in your research is important. not all information is created equally. there are better sources for different types of information needs., once you have defined your topic and research question consider:, the information cycle (how information is produced and disseminated over time after an event) , type of source, primary, secondary or tertiary, scholarly/peer-reviewed or not, level of evidence, systematic review, randomized controlled trials.

• Know Your Sources

This tool provides a brief description of each type of source and breaks down 6 factors of what to consider when selecting a source.

Click the image below for more information.

Primary, Secondary and Tertiary Sources

Sources fall into three main categories, primary, secondary and tertiary., primary sources give firsthand accounts or direct evidence regarding the event. they are writings contemporary to what is being researched., secondary sources discuss information presented elsewhere. it is created later, after the event, by someone who did not participate or experience the event. most scholarly articles and books are secondary sources., tertiary sources consolidate and summarize primary and secondary sources. for example, encyclopedias and factbooks are considered tertiary (although some may be secondary)..

• Examples of Types of Sources

Scholarly Articles and Peer-Review

Sources are created for different audiences. sources created by scholars for other scholars are often published in scholarly/peer-reviewed journals., peer-review is a vetting process a source may go through. the peer-review process involves an author submitting their work for review, then a group of their "peers" (other people working in the same field) evaluate the work for quality and meeting scientific standards. then the work is returned to the original author for edits/revisions. then the work is (hopefully) re-submitted and accepted for publishing., the peer-review process is not perfect and academic publishing is highly competitive so problems do occur. you can read about some of the conversations about revising peer-review in the following articles:  scientists aim to pull peer review out of the 17th century ,  the future of peer review and  when reviewing goes wrong: the ugly side of peer review ..

image from  https://undsci.berkeley.edu/article/howscienceworks_16

There are several features of scholarly sources that distinguish them from popular sources including:

Written for experts by experts, use of professional language for the discipline, based on original research or analysis of previous research, contains citations, no attractive packaging or ads, scientific paper format (abstract, introduction, methods, results, discussion, conclusion, references), peer-reviewed, published by academic publisher.

Knowing how to read a scholarly article is a very important skill. Knowing these steps can help you better understand the research you find. 

Levels of Evidence

In health sciences and medicine, sources also have a level of evidence based on the type of research conducted for the work. the levels of evidence are described in a pyramid with the lowest level of evidence at the bottom and the highest level of evidence at the top. the amount of sources meeting the criteria of these levels decreases as the levels increase so that there are a lot more level vii sources than level i sources., unitypoint health uses the iowa model for evidence-based practice:  https://uihc.org/iowa-model-revised-evidence-based-practice-promote-excellence-health-care.

• Rating System for the Hierarchy of Evidence for Intervention/Treatment Questions

The level of evidence can usually be discovered in the methods section of the article. Some authors will state exactly what type of study the article is about and for other sources, the reader will have to determine the study type.

Use this chart to help determine the level, level i is a systematic review and level vii is an expert opinion..

• Level of evidence flow chart

Another library has created a guide with even more information. Check it out at the link below.

• EBP Tool Kit from Winona State's Darrell W. Krueger Library

You can also use this handy chart from the University of New Mexico.

• Evidence Pyramid - Levels of Evidence

Theoretical Models and Frameworks

Theoretical models and frameworks create a structure and vision for the study. you can think of these as blueprints for the study. a scientific study will use a theoretical framework or model to guide the design of the study. , types of clinical research, action research, cross-sectional, descriptive, experimental , exploratory, longitudinal, meta-analysis, mixed methods, observational, philosophical, how to support research with theoretical and conceptual frameworks, learn more at  https://rushu.libguides.com/c.phpg=694134.

• << Previous: ...Select a Topic and Make a Research Plan?
• Next: ...Evaluate Information? >>
• Last Updated: Aug 29, 2022 8:07 AM
• URL: https://methodistcol.libguides.com/howdoi

Reference management. Clean and simple.

Types of research papers

Analytical research paper

Argumentative or persuasive paper, definition paper, compare and contrast paper, cause and effect paper, interpretative paper, experimental research paper, survey research paper, frequently asked questions about the different types of research papers, related articles.

There are multiple different types of research papers. It is important to know which type of research paper is required for your assignment, as each type of research paper requires different preparation. Below is a list of the most common types of research papers.

➡️ Read more:  What is a research paper?

In an analytical research paper you:

• pose a question
• collect relevant data from other researchers
• analyze their different viewpoints

You focus on the findings and conclusions of other researchers and then make a personal conclusion about the topic. It is important to stay neutral and not show your own negative or positive position on the matter.

The argumentative paper presents two sides of a controversial issue in one paper. It is aimed at getting the reader on the side of your point of view.

You should include and cite findings and arguments of different researchers on both sides of the issue, but then favor one side over the other and try to persuade the reader of your side. Your arguments should not be too emotional though, they still need to be supported with logical facts and statistical data.

Tip: Avoid expressing too much emotion in a persuasive paper.

The definition paper solely describes facts or objective arguments without using any personal emotion or opinion of the author. Its only purpose is to provide information. You should include facts from a variety of sources, but leave those facts unanalyzed.

Compare and contrast papers are used to analyze the difference between two:

Make sure to sufficiently describe both sides in the paper, and then move on to comparing and contrasting both thesis and supporting one.

Cause and effect papers are usually the first types of research papers that high school and college students write. They trace probable or expected results from a specific action and answer the main questions "Why?" and "What?", which reflect effects and causes.

In business and education fields, cause and effect papers will help trace a range of results that could arise from a particular action or situation.

An interpretative paper requires you to use knowledge that you have gained from a particular case study, for example a legal situation in law studies. You need to write the paper based on an established theoretical framework and use valid supporting data to back up your statement and conclusion.

This type of research paper basically describes a particular experiment in detail. It is common in fields like:

Experiments are aimed to explain a certain outcome or phenomenon with certain actions. You need to describe your experiment with supporting data and then analyze it sufficiently.

This research paper demands the conduction of a survey that includes asking questions to respondents. The conductor of the survey then collects all the information from the survey and analyzes it to present it in the research paper.

➡️ Ready to start your research paper? Take a look at our guide on how to start a research paper .

In an analytical research paper, you pose a question and then collect relevant data from other researchers to analyze their different viewpoints. You focus on the findings and conclusions of other researchers and then make a personal conclusion about the topic.

The definition paper solely describes facts or objective arguments without using any personal emotion or opinion of the author. Its only purpose is to provide information.

Cause and effect papers are usually the first types of research papers that high school and college students are confronted with. The answer questions like "Why?" and "What?", which reflect effects and causes. In business and education fields, cause and effect papers will help trace a range of results that could arise from a particular action or situation.

This type of research paper describes a particular experiment in detail. It is common in fields like biology, chemistry or physics. Experiments are aimed to explain a certain outcome or phenomenon with certain actions.

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Clarkson College LibAnswers

How do i determine the level of evidence of an article, johns hopkins nursing ebp: levels of evidence.

Level I Experimental study, randomized controlled trial (RCT) Systematic review of RCTs, with or without meta-analysis

Level II Quasi-experimental Study Systematic review of a combination of RCTs and quasi-experimental, or quasi-experimental studies only, with or without meta-analysis.

Level III Non-experimental study Systematic review of a combination of RCTs, quasi-experimental and non-experimental, or non-experimental studies only, with or without meta-analysis. Qualitative study or systematic review, with or without meta-analysis

Level IV Opinion of respected authorities and/or nationally recognized expert committees/consensus panels based on scientific evidence.     Includes:          - Clinical practice guidelines          - Consensus panels

Level V Based on experiential and non-research evidence.     Includes:       - Literature reviews       - Quality improvement, program or financial evaluation       - Case reports       - Opinion of nationally recognized expert(s) based on experiential evidence

From   Johns   Hopkins   nursing  evidence-based practice : Models and Guidelines Dearholt, S., Dang, Deborah, & Sigma Theta Tau International. (2012).  Johns Hopkins Nursing Evidence-based Practice : Models and Guidelines .

The links below will provide further information. 

Links & Files

• The JBI Levels of Evidence
• Tutorial - Identifying Types of Evidence
• Tutorial - Using the Hierarchy of Evidence
• Tutorial - Understanding Study Design
• Winona State University Level of Evidence page
• Last Updated Jan 11, 2022
• Answered By Anne Heimann

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Evidence-Based Practice for Nursing: Evaluating the Evidence

• What is Evidence-Based Practice?
• Asking the Clinical Question
• Finding Evidence
• Evaluating the Evidence
• Articles, Books & Web Resources on EBN

Evaluating Evidence: Questions to Ask When Reading a Research Article or Report

For guidance on the process of reading a research book or an article, look at Paul N. Edward's paper,  How to Read a Book  (2014) . When reading an article, report, or other summary of a research study, there are two principle questions to keep in mind:

1. Is this relevant to my patient or the problem?

• Once you begin reading an article, you may find that the study population isn't representative of the patient or problem you are treating or addressing. Research abstracts alone do not always make this apparent.
• You may also find that while a study population or problem matches that of your patient, the study did not focus on an aspect of the problem you are interested in. E.g. You may find that a study looks at oral administration of an antibiotic before a surgical procedure, but doesn't address the timing of the administration of the antibiotic.
• The question of relevance is primary when assessing an article--if the article or report is not relevant, then the validity of the article won't matter (Slawson & Shaughnessy, 1997).

2. Is the evidence in this study valid?

• Validity is the extent to which the methods and conclusions of a study accurately reflect or represent the truth. Validity in a research article or report has two parts: 1) Internal validity--i.e. do the results of the study mean what they are presented as meaning? e.g. were bias and/or confounding factors present? ; and 2) External validity--i.e. are the study results generalizable? e.g. can the results be applied outside of the study setting and population(s) ?
• Determining validity can be a complex and nuanced task, but there are a few criteria and questions that can be used to assist in determining research validity. The set of questions, as well as an overview of levels of evidence, are below.

For a checklist that can help you evaluate a research article or report, use our checklist for Critically Evaluating a Research Article

• How to Critically Evaluate a Research Article

How to Read a Paper--Assessing the Value of Medical Research

Evaluating the evidence from medical studies can be a complex process, involving an understanding of study methodologies, reliability and validity, as well as how these apply to specific study types. While this can seem daunting, in a series of articles by Trisha Greenhalgh from BMJ, the author introduces the methods of evaluating the evidence from medical studies, in language that is understandable even for non-experts. Although these articles date from 1997, the methods the author describes remain relevant. Use the links below to access the articles.

• How to read a paper: Getting your bearings (deciding what the paper is about) Not all published research is worth considering. This provides an outline of how to decide whether or not you should consider a research paper. more... less... Greenhalgh, T. (1997b). How to read a paper. Getting your bearings (deciding what the paper is about). BMJ (Clinical Research Ed.), 315(7102), 243–246.
• Assessing the methodological quality of published papers This article discusses how to assess the methodological validity of recent research, using five questions that should be addressed before applying recent research findings to your practice. more... less... Greenhalgh, T. (1997a). Assessing the methodological quality of published papers. BMJ (Clinical Research Ed.), 315(7103), 305–308.
• How to read a paper. Statistics for the non-statistician. I: Different types of data need different statistical tests This article and the next present the basics for assessing the statistical validity of medical research. The two articles are intended for readers who struggle with statistics more... less... Greenhalgh, T. (1997f). How to read a paper. Statistics for the non-statistician. I: Different types of data need different statistical tests. BMJ (Clinical Research Ed.), 315(7104), 364–366.
• How to read a paper: Statistics for the non-statistician II: "Significant" relations and their pitfalls The second article on evaluating the statistical validity of a research article. more... less... Greenhalgh, T. (1997). Education and debate. how to read a paper: Statistics for the non-statistician. II: "significant" relations and their pitfalls. BMJ: British Medical Journal (International Edition), 315(7105), 422-425. doi: 10.1136/bmj.315.7105.422
• How to read a paper. Papers that report drug trials more... less... Greenhalgh, T. (1997d). How to read a paper. Papers that report drug trials. BMJ (Clinical Research Ed.), 315(7106), 480–483.
• How to read a paper. Papers that report diagnostic or screening tests more... less... Greenhalgh, T. (1997c). How to read a paper. Papers that report diagnostic or screening tests. BMJ (Clinical Research Ed.), 315(7107), 540–543.
• How to read a paper. Papers that tell you what things cost (economic analyses) more... less... Greenhalgh, T. (1997e). How to read a paper. Papers that tell you what things cost (economic analyses). BMJ (Clinical Research Ed.), 315(7108), 596–599.
• Papers that summarise other papers (systematic reviews and meta-analyses) more... less... Greenhalgh, T. (1997i). Papers that summarise other papers (systematic reviews and meta-analyses). BMJ (Clinical Research Ed.), 315(7109), 672–675.
• How to read a paper: Papers that go beyond numbers (qualitative research) A set of questions that could be used to analyze the validity of qualitative research more... less... Greenhalgh, T., & Taylor, R. (1997). Papers that go beyond numbers (qualitative research). BMJ (Clinical Research Ed.), 315(7110), 740–743.

Levels of Evidence

In some journals, you will see a 'level of evidence' assigned to a research article. Levels of evidence are assigned to studies based on the methodological quality of their design, validity, and applicability to patient care. The combination of these attributes gives the level of evidence for a study.  Many systems for assigning levels of evidence exist.  A frequently used system in medicine is from the  Oxford Center for Evidence-Based Medicine .  In nursing, the system for assigning levels of evidence is often from Melnyk & Fineout-Overholt's 2011 book,  Evidence-based Practice in Nursing and Healthcare: A Guide to Best Practice .  The Levels of Evidence below are adapted from Melnyk & Fineout-Overholt's (2011) model.  

Uses of Levels of Evidence : Levels of evidence from one or more studies provide the "grade (or strength) of recommendation" for a particular treatment, test, or practice. Levels of evidence are reported for studies published in some medical and nursing journals. Levels of Evidence are most visible in Practice Guidelines, where the level of evidence is used to indicate how strong a recommendation for a particular practice is. This allows health care professionals to quickly ascertain the weight or importance of the recommendation in any given guideline. In some cases, levels of evidence in guidelines are accompanied by a Strength of Recommendation.

About Levels of Evidence and the Hierarchy of Evidence : While Levels of Evidence correlate roughly with the hierarchy of evidence (discussed elsewhere on this page), levels of evidence don't always match the categories from the Hierarchy of Evidence, reflecting the fact that study design alone doesn't guarantee good evidence. For example, the systematic review or meta-analysis of randomized controlled trials (RCTs) are at the top of the evidence pyramid and are typically assigned the highest level of evidence, due to the fact that the study design reduces the probability of bias  ( Melnyk , 2011),  whereas the weakest level of evidence is the  opinion from authorities and/or reports of expert committees.  However, a systematic review may report very weak evidence for a particular practice and therefore the level of evidence behind a recommendation may be lower than the position of the study type on the Pyramid/Hierarchy of Evidence.

About Levels of Evidence and Strength of Recommendation : The fact that a study is located lower on the Hierarchy of Evidence does not necessarily mean that the strength of recommendation made from that and other studies is low--if evidence is consistent across studies on a topic and/or very compelling, strong recommendations can be made from evidence found in studies with lower levels of evidence, and study types located at the bottom of the Hierarchy of Evidence. In other words, strong recommendations can be made from lower levels of evidence.

For example: a case series observed in 1961 in which two physicians who noted a high incidence (approximately 20%) of children born with birth defects to mothers taking thalidomide resulted in very strong recommendations against the prescription and eventually, manufacture and marketing of thalidomide. In other words, as a result of the case series, a strong recommendation was made from a study that was in one of the lowest positions on the hierarchy of evidence.

Hierarchy of Evidence for Quantitative Questions

The pyramid below represents the hierarchy of evidence, which illustrates the strength of study types; the higher the study type on the pyramid, the more likely it is that the research is valid. The pyramid is meant to assist researchers in prioritizing studies they have located to answer a clinical or practice question. 

For clinical questions, you should try to find articles with the highest quality of evidence. Systematic Reviews and Meta-Analyses are considered the highest quality of evidence for clinical decision-making and should be used above other study types, whenever available, provided the Systematic Review or Meta-Analysis is fairly recent. 

As you move up the pyramid, fewer studies are available, because the study designs become increasingly more expensive for researchers to perform. It is important to recognize that high levels of evidence may not exist for your clinical question, due to both costs of the research and the type of question you have.  If the highest levels of study design from the evidence pyramid are unavailable for your question, you'll need to move down the pyramid.

While the pyramid of evidence can be helpful, individual studies--no matter the study type--must be assessed to determine the validity.

Hierarchy of Evidence for Qualitative Studies

Qualitative studies are not included in the Hierarchy of Evidence above. Since qualitative studies provide valuable evidence about patients' experiences and values, qualitative studies are important--even critically necessary--for Evidence-Based Nursing. Just like quantitative studies, qualitative studies are not all created equal. The pyramid below  shows a hierarchy of evidence for qualitative studies.

Adapted from Daly et al. (2007)

Help with Research Terms & Study Types: Cut through the Jargon!

• CEBM Glossary
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2025 Research Designations

• Research Designations

The 2025 Carnegie Classifications will include research designations as separate listings from the Basic Classification. There will be three research groupings, all of which will be set by a threshold. Thresholds may be changed in future years; updated methodology will be shared ahead of each classification release.

In 2025, the Carnegie Classifications will use the higher of either a three-year average (2021, 2022, 2023) or most recent single year data (2023). In future releases, the classifications will use only a three-year average. Spending data will be taken from the Higher Education Research and Development (HERD) Survey for FY2021, FY2022, and FY2023. Doctorate production will be taken from data reported to the National Center for Education Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) for academic years 2020-21, 2021-22, and 2022-23. Research doctorates include all degrees reported as a Doctor’s Degree – Research/Scholarship in IPEDS, following the  IPEDS definition .

These changes are part of a series of updates for the Carnegie Classifications. For more information about the changes, please see the press release and the FAQ .

Research 1: Very High Spending and Doctorate Production

On average in a single year, these institutions spend at least $50 million on research & development and produce at least 70 research doctorates.

Research 2: High Spending and Doctorate Production

On average in a single year, these institutions spend at least $5 million on research & development and produce at least 20 research doctorates.

Research Colleges and Universities

On average in a single year, these institutions spend at least $2.5 million on research & development. Institutions that are in the R1 or R2 categories are not included.

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As an important foundation of cargo transportation, logistics plays a vital role in developing international trade. Based on the international logistics performance index (LPI) and the sample data of the “Belt and Road” initiative countries from 2011 to 2022, this paper uses the extended trade gravity model to explore the impact of the logistics performance of the “Belt and Road” initiative countries on China’s import and export trade. The empirical results show that the improvement of the logistics performance level of the countries along the “Belt and Road” Initiative has a certain role in promoting the growth of China’s trade volume to the country, and the improvement of LPI has a more significant positive impact on China’s import and export to large-scale countries along the route. Finally, according to the analysis of empirical results, this paper puts forward specific suggestions to promote the development of logistics performance and import and export trade, which provides some reference value for implementing the “Belt and Road” initiative and improving national logistics and trade level.

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Introduction.

In the context of 'globalization', China and the countries along the “Belt and Road” Initiative have ushered in a high-speed development stage of comprehensive cooperation (Khan et al. 2022a ), working together to promote a higher level of trade facilitation development trend and promote the further expansion of trade scale (Khan et al. 2022b ). The proportion of the trade scale between China and the “Belt and Road” initiative countries in China’s overall foreign trade continues to grow (Mena et al. 2022 ), reaching 34.7% by 2022. The trade between China and the “Belt and Road” initiative countries is mainly concentrated in Southeast Asia, focusing on West Asia and South Asia (Qin 2022 ). From an overall perspective, in 2022, China’s trade with ASEAN accounted for 50.3% of the trade of the “Belt and Road” initiative countries. And the trade imbalance between China and the “Belt and Road” initiative countries is becoming more and more serious (Wang and Liu 2022 ).

Since 2007, the World Bank has published the “Logistics Performance Index Report” every two years to measure the level of logistics in countries worldwide with the LPI (Hausman et al. 2013 ). The progress of the logistics industry drives the development of manufacturing, finance, and other industries, promotes the coordinated development of upstream and downstream enterprises, and promotes the formation of a complete industrial chain and supply chain (Göçer et al. 2022 ). The logistics performance of the “Belt and Road” initiative countries has become an important factor in promoting import and export between China and the “Belt and Road” initiative countries (Pan et al. 2022 ). With the continuous advancement of the 'economic integration' process, logistics connects the needs of trade between countries, is an essential guarantee for the smooth operation of the supply chain (Yang 2023 ), and plays a vital role in the country’s economic development (Yingfei et al. 2022 ). Therefore, the research on the relationship between international logistics performance and the development of import and export trade has certain practical significance.

Due to the incalculable impact of the 2008 financial crisis on the world economy, this paper selects 2011–2022 as the time interval for research to avoid the effect on the accuracy of empirical results. Combined with data availability, taking 61 countries along the Belt and Road Initiative as an example, this paper profoundly analyzes the relationship between international logistics performance and national import and export trade. It systematically analyzes the impact of different indicators of international logistics performance on international trade import and export and more accurately controls the adjustment direction of resource allocation to provide some reference value for the implementation of the “Belt and Road” initiative of China and provide some reference for the balance of national logistics system and trade relations.

Compared with previous studies, the innovation of this paper is mainly reflected in the following aspects: Firstly, the interaction mechanism between international logistics performance and import and export trade is analyzed. This paper analyzes the interaction mechanism between international logistics performance and import and export trade through previous data collection. Secondly, the fixed effect model is used to test the impact of the improvement of the logistics performance level of the countries along the “Belt and Road” on the trade volume, and to explore the impact of international logistics performance on the import and export of countries of different sizes and the differences. Thirdly, the relationship between international logistics performance and import and export trade is tested by using the methods of 'eliminating outlier samples', 'reducing sample interval', 'shrinking tail processing' and 'lagging one period of explained variables', and the conclusions are summarized, and specific and feasible development suggestions are put forward.

The research contributions of this paper can be summarized as follows: Firstly, it further enriches the research on international logistics performance and import and export trade. The research of related fields is mainly related to the relationship between logistics performance and export. This paper takes import and export as the research object, which has a specific reference value for the overall development of the country and the region. Secondly, this paper not only explores the relationship between international logistics performance and import and export trade but also divides different countries according to their scale and explores the various impacts on countries of different scales, which provides a specific reference for the direction of national logistics and trade investment. Thirdly, this paper discusses the relationship between international logistics performance and the development of import and export trade, and puts forward specific suggestions to promote the improvement of international logistics performance and trade volume, which helps provide some reference value for the development of related fields.

The section 2 reviews the literature on logistics performance and international trade, and provides some theoretical support for the article. Section 3 explains the construction of the gravity model data sources, the variables, and the division of the national scale. In section 4, descriptive statistics, LPI comprehensive index regression, and LPI sub-index regression are used, and systematic analysis is carried out according to the results of empirical data. Section 5 conducts a robustness test. Section 6 summarizes the conclusions and puts forward specific suggestions based on the empirical analysis.

Literature review

Logistics performance is the key to supporting trade growth and the main factor determining a country’s economic growth (Cui et al. 2022 ). Bhukiya and Patel ( 2023 ) and Huong et al. ( 2024 ) believed that logistics performance promotes international trade. Barakat et al. ( 2023 ) demonstrated that the improvement of logistics performance helps to increase national trade openness and reduce trade costs. Jayathilaka et al. ( 2022 ) analyzed the impact of gross domestic product (GDP) and LPI on international trade based on 142 countries, and verified the positive role of LPI in promoting international trade, which is more significant in Asia, Europe and Oceania. Çelebi ( 2019 ) believes that logistics performance will promote the development of trade, and the efficiency of logistics system is an important factor affecting bilateral trade. Based on the sample data of 10 countries along the China-Europe Express from 2015 to 2019, Zhong and Zhou ( 2022 ) demonstrated that the improvement of international logistics performance has promoted the increase of import and export trade in Guangdong Province. Liu ( 2022 ) selected the data of 12 provinces and regions in western China from 2015 to 2020 to explore the impact of cross-border logistics performance on the competitiveness of cross-border agricultural products trade, and found that the development of cross-border logistics is conducive to improving the development of cross-border agricultural products trade in western China. The above research shows that: in the context of economic integration, the development of international logistics performance has promoted the improvement of the international trade environment and improved the convenience of international trade. There is a positive correlation between international logistics performance and international trade.

Import and export trade has a certain feedback effect on the development of logistics industry (Yang 2010 ). Guo ( 2018 ) used the panel data of 31 provinces in China from 1997 to 2016 to empirically test the role of import and export trade in promoting the development of the logistics industry, and the impact of exports on the development of the logistics industry is significantly greater than the role of imports. At the same time, due to differences in geographical location and resource endowments, only imports in the central and eastern regions of China promote the development of the logistics industry, and the western region is not significant. Zhan et al. ( 2019 ) found that the scale effect, export efficiency effect and export structure effect of export trade in the core area of the “Belt and Road” have promoted the development of the logistics industry. Wang and Wang ( 2021 ) found that the trade in the core area of “Belt and Road” can promote the growth and agglomeration of the logistics industry, and the export scale effect is the main factor to promote the growth of the logistics industry. The expansion of international trade scale, the improvement of trade efficiency and the improvement of trade structure have also promoted the improvement of international logistics performance. Based on the co-integration model of time series data from 1989 to 2012, Wang ( 2015 ) found that there is a co-integration relationship between logistics development and energy consumption, foreign trade and urbanization level, and this co-integration relationship has a long stability. Yang et al. ( 2019 ) found that the logistics development between China and ASEAN countries is the reason that affects the development of each other’s trade through the Granger causality test. Guo et al. ( 2018 ) studied the development of China’s logistics industry and foreign trade in the past 40 years of reform and opening up, and found that there is a long-term and stable coordinated development relationship between the two. In order to promote the sustainable development of the two, it can be achieved by optimizing the business environment, promoting the support of the coordinated development of modern logistics and foreign trade, improving the quality of logistics infrastructure and customs operation efficiency, and accelerating the informatization and standardization of logistics industry.

There are some differences in the impact of international logistics performance on countries with different income levels, different trade facilitation levels, and different population sizes (Fan and Yu 2015 ). See et al. ( 2024 ) found that countries with higher income levels have better logistics performance. Çelebi ( 2019 ) believes that income level is an important factor in the impact of logistics performance on trade volume. Trade facilitation will have different effects according to per capita income level, and low-income economies with higher logistics level will gain more benefits than high-income economies. Compared with the increase of logistics level in low-income countries, the increase of trade volume will be promoted, and the import volume of middle and high-income countries will benefit more from the improvement of logistics performance. Kumari and Bharti ( 2021 ) studied the impact of country size on trade and logistics performance based on population size, and found that the degree of LPI to improve related trade growth is the highest in medium-sized countries, followed by small-scale countries. Among the sub-indicators of LPI, cargo tracking ability and timeliness have the greatest impact on the trade of small-scale countries, and the convenience and timeliness of arranging international freight transportation have the greatest impact on medium-sized countries.

In summary, with the deepening of the globalization of the supply chain and industrial chain, import and export trade are moving towards lower cost and higher efficiency. International logistics performance and import and export trade promote each other and jointly drive national economic development. The impact of international performance on the trade of different countries has certain differences. However, there are still few studies on the impact of logistics performance on the trade of countries of different sizes. Therefore, this paper divides the “Belt and Road” initiative countries according to population size, further explores the impact of international logistics performance on import and export trade, and provides a reference for the development of the “Belt and Road” initiative countries and the trade between nations.

Data selection and model construction

In order to improve the trade level of the “Belt and Road” initiative countries, this paper studies the impact of international logistics performance of the “Belt and Road” initiative countries on China’s import and export trade, constructs an extended gravity model, and introduces the LPI into the model. At the same time, according to the population size, the countries along the “Belt and Road” are divided into three categories: large, medium and small, to explore the impact of international logistics performance on the import and export of countries of different sizes and the differences.

Data processing and variable setting

This study takes 2011–2022 as the time interval of the study. The data mainly come from the World Bank WDI database. In view of the fact that the data published by the World Bank has been updated to 2022, but there are missing data in individual years, such as LPI, since the World Bank releases the logistics performance index every two years, in order to ensure the continuity of the data, the missing data of this part is filled by linear prediction using stata15 software. In order to avoid the impact of unit differences between indicators on the experimental results, the gross national product of the “Belt and Road” initiative countries, China’s imports and exports to the “Belt and Road” initiative countries, the distance from the “Belt and Road” initiative countries, China’s gross national product, the comprehensive index of international logistics performance, the score of cargo tracking ability, the score of logistics serviceability, the score of international freight transportation that is easy to arrange competitive prices, the score of customs clearance process efficiency, the score of the expected time of goods to reach the consignee frequency and the score of transportation-related infrastructure quality are standardized by stata15. Variables are set as follows:

Explained variable: China’s trade volume with the “Belt and Road” initiative countries (billions of dollars).

Explanatory variable: international logistics performance of the “Belt and Road” initiative countries. Sub-indicators: goods tracking ability score, logistics serviceability score, easy-to-arrange price competitive international freight score, customs clearance process efficiency score, goods expected time to reach the consignee frequency score, and transportation-related infrastructure quality score.

Control variables: distance from the “Belt and Road” initiative countries (kilometers), gross national product of the “Belt and Road” initiative countries (billions of dollars), gross national product of China (billions of dollars), the ratio of total imports and exports of goods and services to GDP of the sample countries, whether it is adjacent to China, and whether it has joined the WTO.

Data sources and processing instructions

According to the model setting and variable definition, the variable name, economic implications, variable value, data source and expected impact on trade volume of international logistics performance and its sub-indicators and control variables on trade volume is shown in Table 1 . If the expected impact on trade volume is positive, it is expressed as '+', and vice versa.

Since the fixed effect model is used for regression analysis while controlling the year and time, all variables should change with time. This paper uses the product of the distance between China and the “Belt and Road” initiative countries and the Brent crude oil price of the year to represent the distance, so that the distance can change with time, which enhances the feasibility of the model. There are 65 countries and regions along the “Belt and Road” marked by the 'China Belt and Road Network'. However, due to the lack of data in Brunei, Timor-Leste, Palestine and other countries, combined with the availability of data, this study selects the “Belt and Road” initiative countries: 40 countries in Asia, 20 countries in Europe and one country in Africa, a total of 61 countries from 2011 to 2022 sample data for empirical research.

According to the average population data of the “Belt and Road” initiative countries from 2011 to 2022, the countries with the top 25% of the population are classified as large-scale countries, the latter 25% are classified as small-scale countries, and 25–75% are classified as medium-scale countries. The specific division results are shown in Table 2 .

Model construction

The gravitational model is derived from the law of universal gravitation proposed by the British physicist Newton. It was originally used to explain the interaction between objects and was later cited in the field of international trade. It is used to measure the relationship between the trade volume between the two countries and their economic scale (Zhong and Zhou 2022 ). The formula can be expressed as:

Formula (1) is transformed into logarithmic form and the random error term can be expressed as:

The above equation \({{TRADE}}_{{ij}}\) represents the trade volume between country i and country j, \({X}_{i}\) and \({X}_{j}\) represent the economic aggregate of country i and country j respectively, \({{DIS}}_{{ij}}\) represents the geographical distance between the two economies of country i and country j, \({\beta }_{0}\) represents the parameters to be estimated in the model, and ε represents the random error term of the model.

In the gravity model setting in the field of international trade, the trade volume between the two countries is negatively correlated with the distance between the two countries, and positively correlated with the total economic volume of the two countries. On the basis of the basic gravity model, combined with the existing research, the international logistics performance index released by the World Bank is introduced into the gravity model, and the control variables are added to expand the model. The control variables include: DIS, GDPJ, GDPC, OPEN, BORDER and WTO, in which OPEN is an endogenous variable, BORDER and WTO are dummy variables.

The extended gravity model can be expressed as follows:

Each sub-index of LPI as an alternative index of LPI into the extended gravity model can be expressed as:

Empirical analyses

Analysis of statistical index results.

The descriptive statistical results are shown in Table 3 . According to the results in the table, there are great differences in the data results of the “Belt and Road” initiative countries. First of all, there is a big difference in China’s import and export to the “Belt and Road” initiative countries: China’s import and export to Bhutan, Maldives, Bosnia and Herzegovina, North Macedonia and other countries remained low from 2011 to 2022, with an average annual import and export value of no more than $300 million. Trade with Singapore, India, Russia and other countries have remained at more than USD 50 billion since 2011. Secondly, the population size of the “Belt and Road” initiative countries is significantly different: the population of Maldives, Bhutan, Montenegro and other countries is less than one million between 2011 and 2022, while India’s population remains above one billion. Third, the scores of various indicators related to logistics in countries along the route are not the same: from the perspective of the comprehensive index of logistics performance, Singapore and other countries have maintained a score of more than 4, and the comprehensive ranking is among the top ten in the world, while Mongolia, Myanmar, Laos, Tajikistan, Turkmenistan and other countries have a low logistics level ranking of 100. From the perspective of each sub-index, the differences between countries are obvious, and the development status of the “Belt and Road” initiative countries is uneven.

LPI comprehensive index regression

On the basis of descriptive statistics, this paper further uses Stata15.0 software to conduct regression analysis on the panel data of the “Belt and Road” initiative countries from 2011 to 2022. Since the data cross section (N) > time series (T), it is a short panel and does not require a unit root test. Through collinearity diagnosis, it was found that the VIF values of each index were less than 10, indicating that there was no multicollinearity in the data. The results of the Hausman test are shown in Table 4 , and the P value is 0.0017, which is less than 0.1. Therefore, the results are significant. The original hypothesis that the panel data model is a random effect model is rejected, and the fixed effect model is supported. The fixed effect model is used to analyze the data from 2011 to 2022 by controlling the country and time at the same time. The regression results are shown in Table 5 .

According to the regression results, the impact of international logistics performance of the “Belt and Road” initiative countries on China’s import and export trade is as follows:

According to the regression results in Table 5 , the impact of LPI on China’s import and export trade is significantly positive under the condition of 10%, indicating that the higher the LPI of the “Belt and Road” initiative countries, the more conducive to the trade between China and the country.

In the regression results of endogenous variables, the coefficient of DIS is −0.116, which is significant at the 1% level. Therefore, the distance between China and the “Belt and Road” initiative countries has a significant negative impact on China’s trade volume. The farther the distance is, the more unfavorable it is for China’s import and export to the Belt and Road Initiative countries. In China’s international trade, distance cost is still an important influencing factor. The economic volume coefficient of the countries along the route is 0.803, indicating that the economic volume of the countries along the route has a certain impact on China’s imports and exports to the country. The higher the GDP is, the higher the economic development level of the country is, and the higher the corresponding consumption level is, thus driving China’s import and export to the country. China’s economic volume coefficient is 0.1, which is significantly positive at the 1% level. Therefore, the improvement of China’s economic volume will significantly promote the growth of international trade volume. In addition, the degree of opening to the outside world has a significant role in promoting international trade, with a coefficient of 0.223, indicating that the higher the degree of opening to the outside world of countries along the route will be conducive to China’s import and export to the country.

Through the results of dummy variable data, it can be seen that the BORDER and WTO coefficients are-0.048 and 0.011, respectively, and the BORDER is significant at the level of 10%, indicating that the national border is not conducive to improving China’s import and export of goods. Because the climate environment of the bordering countries is close to China, the differences in resources and production factors may not be obvious enough, so that BORDER is negatively correlated with TRADE. The WTO performance is not significant, indicating that whether to join the WTO organization cannot be used as the strongest factor affecting trade between countries. To a certain extent, the differences in the types of goods traded between China and countries of different sizes will affect the volume of trade between countries. For example, some countries have parallel production with China, which leads to a decrease in trade between China and the country.

The comparison of the results in Table 5 shows that the impact of international logistics performance on China’s import and export to large-scale countries is significantly positive at the 1% level. In addition, the impact of international logistics performance on small and medium-sized countries is negative and insignificant at the 10% level, respectively. To a certain extent, it is due to the small total national demand of small and medium-sized countries. The improvement of international logistics performance has also led to the improvement of the national internal logistics system and promoted the better utilization of national internal resources. Therefore, continuing to invest resources to enhance the international logistics performance level of small and medium-sized countries is not conducive to the growth of import and export trade volume, which is more obvious in small-scale countries, and reasonable allocation of resources is particularly important.

LPI sub-index regression

In order to study the impact of LPI’s specific sub-indicators on China’s import and export to the “Belt and Road” initiative countries, this paper replaces LPI with six sub-indicators TRACE, SERVICE, SHIPMENTS, CLEARANCE, TIME, INFRASTRUCTURE for regression analysis. The results are shown in Table 6 .

From the regression results, it can be seen that TRACE, SHIPMENTS, CLEARANCE and TIME are not significant at the 10% level, indicating that logistics cargo tracking ability, international freight price competitiveness, customs clearance efficiency and logistics timeliness have little impact on China’s import and export to the “Belt and Road” initiative countries. The impact of SERVICE on import and export trade is positive, which is significant at the level of 10%, and INFRASTRUCTURE is positive at the level of 1%. That is, the logistics service capacity and logistics infrastructure quality of the Belt and Road Initiative countries have a greater impact on China’s import and export trade. Logistics service capacity includes inventory capacity, operation capacity and logistics reliability of the logistics system. The progress of logistics inventory capacity and operation capacity will be conducive to the supply of resources and business development of international trade enterprises (Wang 2023 ). The improvement of logistics reliability will increase consumers ‘ online purchase intention to a certain extent and promote the positive development of international trade (Yuan and Zhang 2023 ). The quality of logistics infrastructure is an important guarantee for efficient transportation of goods (Yuan et al. 2023 ). Therefore, China’s trade import and export have a certain dependence on the level of international logistics performance. The improvement of the relevant sub-indicators of the logistics performance index has a certain role in promoting China’s import and export trade.

Through the regression analysis of comprehensive indicators, it can be seen that the impact of international logistics performance on large-scale countries is the most significant. In order to deeply explore the impact of sub-indicators of international logistics performance on large-scale countries, this paper introduces the sub-indicators of international logistics performance indicators into large-scale countries, and replaces LPI for regression analysis. The results are shown in Table 7 .

From the regression results in Table 7 , it can be seen that TRACE and TIME have no significant impact on China’s import and export to large-scale countries. SHIPMENTS is significant at the 5% level. SERVICE, CLEARANCE and INFRASTRUCTURE are significant at the 1% level, and the coefficients are 0.254,0.532,0.485 and 0.449, respectively. The international freight price competitiveness, logistics service capacity, customs clearance efficiency and logistics-related infrastructure level of the “Belt and Road” initiative countries have a significant positive effect on China’s trade import and export.

Robustness test

In order to avoid the influence of extreme values on the empirical results of the selected samples, this paper removes individual outliers and conducts robustness test analysis. Among the 61 sample countries along the “Belt and Road”, China’s annual average import and export volume to Bhutan, North Macedonia, Bosnia and Herzegovina and Moldova from 2011 to 2022 is less than USD 100 million, which has a large gap with the average value of China’s import and export volume to the “Belt and Road” initiative countries. Therefore, in order to avoid the impact of such extreme data on the experimental results, this paper eliminates the sample data of four countries, including Bhutan, North Macedonia, Bosnia and Herzegovina and Moldova, and performs multiple regression analysis on the remaining sample data. The regression results are shown in Table 8 .

Combined with the regression results, it can be seen that the LPI coefficient passed the significance test under the condition of 5%, excluding the influence of sample selection bias on the empirical results of this paper. That is, the international logistics performance of the Belt and Road Initiative countries has a significant role in promoting the growth of trade volume between China and the country.

Since 2020, the new coronavirus epidemic has traumatized the economies of various countries to a certain extent and has had a certain impact on the country’s import and export trade. In order to avoid the interference of such factors on the regression results, the sample time interval is shortened to 2011–2020, and the regression is carried out again. The results are shown in Table 9 , and the estimated coefficient of LPI is significantly positive at the level of 10%, which proves that under the condition of weakening the interference of economic turbulence factors, the improvement of logistics performance level of the “Belt and Road” initiative countries has a significant role in promoting China’s trade import and export, and the conclusions of this paper are still robust.

The data are tailed in the range of 5–95%. The second column in Table 10 shows that the international logistics performance of the “Belt and Road” initiative countries has a positive impact on China’s import and export trade, which is significant at the level of 5%, the coefficient is small, and the main research conclusions have not changed.

Since there may be a certain time difference in the effect of international logistics performance level on import and export trade volume, this paper lags the explained variable TRADE by one period to explore the lag effect of LPI on TRADE, which helps to alleviate the possible two-way causality. The results in Table 11 show that the impact of LPI on TRADE lags one period is consistent with the benchmark results, so the benchmark regression is robust.

Conclusions

By adding the international logistics performance index to the trade gravity model, this paper analyzes the impact of the logistics performance of the “Belt and Road” initiative countries on China’s import and export trade. At the same time, the countries along the 'Belt and Road' are divided into three scales: large, medium and small, to explore the differences in the impact of logistics performance on the import and export of China and these three scale countries. According to the empirical analysis, the following conclusions can be drawn:

First, the improvement of the logistics performance level of the “Belt and Road” initiative countries has a certain role in promoting the increase of trade volume between China and the country. The international logistics performance index of the “Belt and Road” initiative countries has the most significant impact on China’s import and export to large-scale countries. The impact of LPI on China’s import and export trade is significantly positive under the condition of 10%. Therefore, the improvement of the logistics performance index of the 'Belt and Road' initiative countries is conducive to the increase of trade volume between China and the country. The impact of international logistics performance on China’s import and export to large-scale countries is significantly positive at the 1% level, small-scale countries are significantly negative at the 10% level, and medium-scale countries are not significant.

Second, the sub-indicators of the international logistics performance index of the countries along the “Belt and Road” have different degrees of influence on the import and export volume. Among them, logistics service capacity has a significant impact at the level of 10%, and the quality of logistics infrastructure is significant at the level of 1%, and the coefficient is positive. Therefore, the improvement of logistics service capacity and logistics infrastructure quality will help promote the growth of import and export volume. However, TRACE, SHIPMENTS, CLEARANCE and TIME have no significant impact on import and export volume. Therefore, logistics cargo tracking capability, international freight price competitiveness, customs clearance efficiency and logistics timeliness have little impact on China’s import and export to the Belt and Road Initiative countries.

Third, among the sub-indicators of international logistics performance of large-scale countries along the 'Belt and Road', international freight price competitiveness, logistics service capacity, customs clearance efficiency and logistics-related infrastructure level have a significant role in promoting import and export trade, and the impact of cargo tracking capacity and logistics timeliness is not significant. SERVICE, CLEARANCE and INFRASTRUCTURE are significant at the 1% level, with coefficients of 0.532,0.485 and 0.449, respectively, so the impact of logistics service capacity is the greatest.

Practical implications

Based on the relevant conclusions of this paper, it is concluded that the improvement of the international logistics performance of the 'Belt and Road' initiative countries is conducive to promoting the development of China’s international trade, and the factors that have a greater impact on the growth of import and export trade in the sub-indicators of international logistics performance are clarified, which provides a certain basis for the implementation of the 'Belt and Road' initiative. In addition, combined with the research conclusions, targeted suggestions are put forward to provide certain reference values for the improvement of national logistics and trade levels and the implementation direction of the “Belt and Road” initiative.

First, improve logistics performance and reduce trade costs. The regression results show that the international logistics performance of the “Belt and Road” initiative countries has a significant and positive impact on China’s import and export, indicating that the level of logistics performance will promote the economic and trade exchanges between China and the “Belt and Road” initiative countries. The implementation of China’s 'Belt and Road' initiative is of great significance to the deepening of international cooperation. However, due to the large differences in the level of logistics performance among the “Belt and Road” initiative countries, this difference will restrict the development of intra-regional trade to a certain extent, and then weaken the benefits of the “Belt and Road” initiative. Therefore, it is particularly important to give full play to the role of the Asian Infrastructure Investment Bank and the Silk Road Fund to ensure the financial support for the process of improving the logistics performance level of the “Belt and Road” initiative countries. In addition, make full use of advanced digital economy and technology to promote more efficient and lower-cost trade between the “Belt and Road” initiative countries.

Second, strengthen infrastructure facilities and reduce trade barriers between countries along the route. Whether from the LPI comprehensive index regression or the regression results of each sub-index, the coefficient of distance is negative, indicating that the geographical distance between China and the “Belt and Road” initiative countries will have a negative impact on China’s import and export, that is, distance is still an important factor affecting trade costs. However, there are still some problems and obstacles in the logistics facilities of various countries. Therefore, it is necessary to increase the capital investment and investment in various facilities related to logistics, improve the sub-indicators of logistics performance, and improve logistics competitiveness and reduce trade costs by improving infrastructure quality and logistics service capabilities.

Third, improve the logistics performance of large-scale countries and promote the overall development of countries along the “Belt and Road”. Comparing the regression results of the three models of large, medium and small, the LPI passed the significance test of China’s import and export to large-scale countries at the 1% level. International logistics performance has the most significant impact on China’s import and export to large-scale countries along the “Belt and Road”, and the impact of logistics service capacity, customs clearance efficiency and logistics-related infrastructure level in each sub-index is the most significant. That is to say, the improvement of logistics performance of large-scale countries along the route will promote China’s import and export trade to a greater extent. Therefore, in order to promote the high-quality development of the “Belt and Road”, the government can increase investment in infrastructure construction in large-scale countries, promote the development of their import and export trade, and enhance the overall development of the “Belt and Road”.

Limitation and future research

In the research, the “Belt and Road” initiative countries are used as research samples. The sample interval is not broad enough, and the data source has certain limitations. Future research can consider many countries in the world with trade. Secondly, according to the number of populations, this paper divides different countries into three categories: large-scale countries, medium-scale countries and small-scale countries, and explores the different effects of international logistics performance on import and export trade in different countries. In the future, it can be further studied according to other aspects such as national income level, national geographical location and national economic development level. Finally, the fixed effect model is adopted in this paper. The research method is relatively simple, and the selection of control variables and sub-indicators is limited. Future research can try different research methods, add different control variables and sub-indicators to improve the technicality and comprehensiveness of the research. Although there are some limitations in this study, this study has certain positive significance for enriching the literature in the field of international logistics performance and international trade development, and enriches the current knowledge.

Data availability

The data used in the paper were compiled by the authors according to the World Bank Database and Prospective Database. Requests to access these publicly available datasets should be directed to https://d.qianzhan.com/xdata/list/xCxpy5y5xr.html , https://data.worldbank.org.cn/indicator/LP.LPI.OVRL.XQ , https://data.worldbank.org.cn/indicator/LP.LPI.TRAC.XQ , https://data.worldbank.org.cn/indicator/LP.LPI.INFR.XQ , https://data.worldbank.org.cn/indicator/LP.LPI.ITRN.XQ , https://data.worldbank.org.cn/indicator/LP.LPI.LOGS.XQ , https://data.worldbank.org.cn/indicator/LP.LPI.CUST.XQ , https://data.worldbank.org.cn/indicator/LP.LPI.TIME.XQ , https://data.worldbank.org.cn/indicator/NE.TRD.GNFS.ZS .

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Acknowledgements

This research was funded by the Science and Technology Innovation Project of Chongqing Education Commission “Chengdu Chongging Double City Economic Circle Construction”, Grant Number KJCX2020039.

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Weixin Wang

College of Finance and Economics, Sichuan International Studies University, Chongqing, 400031, China

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Conceptualization, Weixin Wang and Jiafu Su; methodology, Qiqi Wu.; software, Jiafu Su.; formal analysis, Bing Li; resources, Weixin Wang; writing—original draft preparation, Weixin Wang; writing—review and editing, Qiqi Wu. All authors have read and agreed to the published version of the manuscript.

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Wang, W., Wu, Q., Su, J. et al. The impact of international logistics performance on import and export trade: an empirical case of the “Belt and Road” initiative countries. Humanit Soc Sci Commun 11 , 1028 (2024). https://doi.org/10.1057/s41599-024-03541-0

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How to cite ChatGPT

Use discount code STYLEBLOG15 for 15% off APA Style print products with free shipping in the United States.

We, the APA Style team, are not robots. We can all pass a CAPTCHA test , and we know our roles in a Turing test . And, like so many nonrobot human beings this year, we’ve spent a fair amount of time reading, learning, and thinking about issues related to large language models, artificial intelligence (AI), AI-generated text, and specifically ChatGPT . We’ve also been gathering opinions and feedback about the use and citation of ChatGPT. Thank you to everyone who has contributed and shared ideas, opinions, research, and feedback.

In this post, I discuss situations where students and researchers use ChatGPT to create text and to facilitate their research, not to write the full text of their paper or manuscript. We know instructors have differing opinions about how or even whether students should use ChatGPT, and we’ll be continuing to collect feedback about instructor and student questions. As always, defer to instructor guidelines when writing student papers. For more about guidelines and policies about student and author use of ChatGPT, see the last section of this post.

Quoting or reproducing the text created by ChatGPT in your paper

If you’ve used ChatGPT or other AI tools in your research, describe how you used the tool in your Method section or in a comparable section of your paper. For literature reviews or other types of essays or response or reaction papers, you might describe how you used the tool in your introduction. In your text, provide the prompt you used and then any portion of the relevant text that was generated in response.

Unfortunately, the results of a ChatGPT “chat” are not retrievable by other readers, and although nonretrievable data or quotations in APA Style papers are usually cited as personal communications , with ChatGPT-generated text there is no person communicating. Quoting ChatGPT’s text from a chat session is therefore more like sharing an algorithm’s output; thus, credit the author of the algorithm with a reference list entry and the corresponding in-text citation.

When prompted with “Is the left brain right brain divide real or a metaphor?” the ChatGPT-generated text indicated that although the two brain hemispheres are somewhat specialized, “the notation that people can be characterized as ‘left-brained’ or ‘right-brained’ is considered to be an oversimplification and a popular myth” (OpenAI, 2023).

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

You may also put the full text of long responses from ChatGPT in an appendix of your paper or in online supplemental materials, so readers have access to the exact text that was generated. It is particularly important to document the exact text created because ChatGPT will generate a unique response in each chat session, even if given the same prompt. If you create appendices or supplemental materials, remember that each should be called out at least once in the body of your APA Style paper.

When given a follow-up prompt of “What is a more accurate representation?” the ChatGPT-generated text indicated that “different brain regions work together to support various cognitive processes” and “the functional specialization of different regions can change in response to experience and environmental factors” (OpenAI, 2023; see Appendix A for the full transcript).

Creating a reference to ChatGPT or other AI models and software

The in-text citations and references above are adapted from the reference template for software in Section 10.10 of the Publication Manual (American Psychological Association, 2020, Chapter 10). Although here we focus on ChatGPT, because these guidelines are based on the software template, they can be adapted to note the use of other large language models (e.g., Bard), algorithms, and similar software.

The reference and in-text citations for ChatGPT are formatted as follows:

• Parenthetical citation: (OpenAI, 2023)
• Narrative citation: OpenAI (2023)

Let’s break that reference down and look at the four elements (author, date, title, and source):

Author: The author of the model is OpenAI.

Date: The date is the year of the version you used. Following the template in Section 10.10, you need to include only the year, not the exact date. The version number provides the specific date information a reader might need.

Title: The name of the model is “ChatGPT,” so that serves as the title and is italicized in your reference, as shown in the template. Although OpenAI labels unique iterations (i.e., ChatGPT-3, ChatGPT-4), they are using “ChatGPT” as the general name of the model, with updates identified with version numbers.

The version number is included after the title in parentheses. The format for the version number in ChatGPT references includes the date because that is how OpenAI is labeling the versions. Different large language models or software might use different version numbering; use the version number in the format the author or publisher provides, which may be a numbering system (e.g., Version 2.0) or other methods.

Bracketed text is used in references for additional descriptions when they are needed to help a reader understand what’s being cited. References for a number of common sources, such as journal articles and books, do not include bracketed descriptions, but things outside of the typical peer-reviewed system often do. In the case of a reference for ChatGPT, provide the descriptor “Large language model” in square brackets. OpenAI describes ChatGPT-4 as a “large multimodal model,” so that description may be provided instead if you are using ChatGPT-4. Later versions and software or models from other companies may need different descriptions, based on how the publishers describe the model. The goal of the bracketed text is to briefly describe the kind of model to your reader.

Source: When the publisher name and the author name are the same, do not repeat the publisher name in the source element of the reference, and move directly to the URL. This is the case for ChatGPT. The URL for ChatGPT is https://chat.openai.com/chat . For other models or products for which you may create a reference, use the URL that links as directly as possible to the source (i.e., the page where you can access the model, not the publisher’s homepage).

Other questions about citing ChatGPT

You may have noticed the confidence with which ChatGPT described the ideas of brain lateralization and how the brain operates, without citing any sources. I asked for a list of sources to support those claims and ChatGPT provided five references—four of which I was able to find online. The fifth does not seem to be a real article; the digital object identifier given for that reference belongs to a different article, and I was not able to find any article with the authors, date, title, and source details that ChatGPT provided. Authors using ChatGPT or similar AI tools for research should consider making this scrutiny of the primary sources a standard process. If the sources are real, accurate, and relevant, it may be better to read those original sources to learn from that research and paraphrase or quote from those articles, as applicable, than to use the model’s interpretation of them.

We’ve also received a number of other questions about ChatGPT. Should students be allowed to use it? What guidelines should instructors create for students using AI? Does using AI-generated text constitute plagiarism? Should authors who use ChatGPT credit ChatGPT or OpenAI in their byline? What are the copyright implications ?

On these questions, researchers, editors, instructors, and others are actively debating and creating parameters and guidelines. Many of you have sent us feedback, and we encourage you to continue to do so in the comments below. We will also study the policies and procedures being established by instructors, publishers, and academic institutions, with a goal of creating guidelines that reflect the many real-world applications of AI-generated text.

For questions about manuscript byline credit, plagiarism, and related ChatGPT and AI topics, the APA Style team is seeking the recommendations of APA Journals editors. APA Style guidelines based on those recommendations will be posted on this blog and on the APA Style site later this year.

Update: APA Journals has published policies on the use of generative AI in scholarly materials .

We, the APA Style team humans, appreciate your patience as we navigate these unique challenges and new ways of thinking about how authors, researchers, and students learn, write, and work with new technologies.

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000

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Focal mechanics and disaster characteristics of the 2024 M 7.6 Noto Peninsula Earthquake, Japan

• Research Article
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• Published: 13 August 2024

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• Guang-qi Chen 1 , 2 ,
• Yan-qiang Wu 3 ,
• Ming-yao Xia 4 &
• Zhi-yuan Li 2  

On January 1, 2024, a devastating M 7.6 earthquake struck the Noto Peninsula, Ishikawa Prefecture, Japan, resulting in significant casualties and property damage. Utilizing information from the first six days after the earthquake, this article analyzes the seismic source characteristics, disaster situation, and emergency response of this earthquake. The results show: 1) The earthquake rupture was of the thrust type, with aftershock distribution showing a north-east-oriented belt-like feature of 150 km. 2) Global Navigation Satellite System (GNSS) and Interferometric synthetic aperture radar (InSAR), observations detected significant westward to north-westward co-seismic displacement near the epicenter, with the maximum horizontal displacement reaching 1.2 m and the vertical uplift displacement reaching 4 m. A two-segment fault inversion model fits the observational data well. 3) Near the epicenter, large Peak Ground Velocity (PGV) and Peak Ground Acceleration (PGA) were observed, with the maxima reaching 145 cm/s and 2681 gal, respectively, and the intensity reached the highest level 7 on the Japanese (Japan Meteorological Agency, JMA) intensity standard, which is higher than level 10 of the United States Geological Survey (USGS) Modified Mercalli Intensity (MMI) standard. 4) The observation of the very rare multiple strong pulse-like ground motion (PLGM) waveform poses a topic worthy of research in the field of earthquake engineering. 5) As of January 7, the earthquake had left 128 deaths and 560 injuries in Ishikawa Prefecture, with 1305 buildings completely or partially destroyed, and had triggered a chain of disasters including tsunamis, fires, slope failures, and road damage. Finally, this paper summarizes the emergency rescue, information dissemination, and other disaster response and management measures taken in response to this earthquake. This work provides a reference case for carrying out effective responses, and offers lessons for handling similar events in the future.

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Acknowledgements

We appreciate the National Research Institute for Earth Science and Disaster Resilience of Japan (NIED), Japan Meteorological Agency (JMA), Geospatial Information Authority of Japan (GSI), Ministry of Land, Infrastructure, Transport and Tourism of Japan (MLIT) and Geological Survey of Japan (GSJ) for providing data and reports in the 2024 M 7.6 Noto Peninsula Earthquake. This study was supported by National High-level Innovative Talents Scientific Research Project in Hebei Province, China (No. 405492), JSPS KAKENHI (No. JP19KK0121), and National Natural Science Foundation of China (Grant No. 42207224).

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Guang-qi Chen

Graduate School of Engineering, Kyushu University, Fukuoka, 819-0395, Japan

Guang-qi Chen & Zhi-yuan Li

The First Monitoring and Application Center, China Earthquake Administration, Tianjin, 300180, China

Yan-qiang Wu

State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China

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Correspondence to Guang-qi Chen .

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Chen, Gq., Wu, Yq., Xia, My. et al. Focal mechanics and disaster characteristics of the 2024 M 7.6 Noto Peninsula Earthquake, Japan. Front. Struct. Civ. Eng. (2024). https://doi.org/10.1007/s11709-024-1111-1

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DOI : https://doi.org/10.1007/s11709-024-1111-1

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Types of Intelligence and Academic Performance: A Systematic Review and Meta-Analysis

Raquel lozano-blasco.

1 Department of Psychology and Sociology, Faculty of Humanities and Science Education, University of Zaragoza, 50001 Zaragoza, Spain

Alberto Quílez-Robres

2 Department of Educational Sciences, Faculty of Human Sciences and Education, University of Zaragoza, 22003 Huesca, Spain

Pablo Usán

3 Department of Psychology and Sociology, Faculty of Education, University of Zaragoza, 50009 Zaragoza, Spain

Carlos Salavera

4 Department of Educational Sciences, Faculty of Humanities and Science Education, University of Zaragoza, 50001 Zaragoza, Spain

Raquel Casanovas-López

5 Instituto de Estudios Interdisciplinarios de la Niñez y la Adolescencia (INEINA), Universidad Nacional, Heredia 40101, Costa Rica

The concept of intelligence has been extensively studied, undergoing an evolution from a unitary concept to a more elaborate and complex multidimensional one. In addition, several research studies have focused their efforts for decades on the study of intelligence as a predictor of academic performance of students at different educational stages, being a stable and highly relevant predictor along with other variables such as executive functions, social context, culture or parental guardianship. Thus, the present study, based on a systematic review and meta-analysis, includes 27 studies with a total sample of 42,061 individuals. The main objective was to analyse the relationship between intelligence and academic performance using different predictive models that include moderating variables such as country of origin, type of intelligence, gender and age. The findings of this research highlight the significant, positive and moderate relationship between intelligence and academic performance (r = 0.367; p < 0.001), highlighting the predictive capacity on school performance when the type of intelligence (general and implicit; 35%) or the country of origin (45%) is taken as a moderating variable, with the explanatory models on age or sex not being significant. Therefore, it can be concluded that intelligence, in addition to being a good predictor of academic performance, is influenced depending on the type of intelligence or theoretical model taken as a reference, and also depending on the country or culture of origin.

1. Introduction

The educational community has traditionally and extensively studied academic performance. This concept is closely related to the teaching–learning process focused on a specific goal: achievement in school ( Von Stumm and Ackerman 2013 ). Therefore, issues such as school success or failure, discouragement and dropout have produced a great deal of research ( Balkis 2018 ). Proof of this would be the study by Nieto Martín ( 2008 ), who reviewed 654 studies conducted between 1970 and 1990. The author stresses that the variables under study and related to academic success have changed over time; for example, intelligence was traditionally studied from a single-factor point of view, but later this approach was expanded and, at present, other variables such as executive functions, motivation or self-esteem and self-efficacy are at the forefront of the study. In addition, the new century has seen the emergence of new methodological variables such as group collaboration, collaborative work, project-based learning and the length of the school day. The literature has traditionally categorised these variables as contextual or personal: socio-environmental variables (family, friends, colleagues), institutional variables (school, school organisation, teachers) and instructional variables (content, methods, tasks). In addition, another group included are cognitive (intelligence, learning styles) and motivational (self-image, goals, values) variables ( Quílez-Robres et al. 2021b ). Therefore, academic performance can be understood as a construct that includes quantitative and qualitative values (quantitative if we talk about numerical measurement, test results and qualitative if we talk about the development of skills, values and competences), related to knowledge, attitudes and values developed by the student in the teaching–learning process” ( Navarro 2003, pp. 15–16 ).

However, this study focuses on the relationship and explanation of academic performance through intelligence, understood as different types of intelligence. There is extensive literature on the relationship, prediction and explanation of intelligence with academic performance. However, studies concerning intelligence have expanded conventional psychometric notions by introducing modalities such as crystallised intelligence, fluid intelligence, emotional intelligence, multiple intelligences, etc., in an attempt to provide greater predictive validity in reference to academic achievement ( Sternberg 2019 ).

The conceptual definitions of intelligence are diverse. For ( Quílez-Robres et al. 2021a ) it consists of the ability to understand and adapt to solve everyday problems. On the other hand, for Plomin and Deary ( 2015 ), intelligence has a biological substrate that varies according to individuals and cultures, being the potential that facilitates learning, planning, reasoning, adaptation and decision-making.

Catell ( 1963 ) differentiated between two kinds of intelligence: fluid intelligence and crystallised intelligence and suggested that intelligence is composed of different capacities that form general intelligence and that are complementary. Crystallised intelligence is the result of education and culture and therefore depends directly on the individual’s prior knowledge and ability to learn ( Nisbett et al. 2012 ) and fluid intelligence with a genetic component is the ability to solve problems through non-verbal abstract reasoning and adaptation to different contexts ( Nisbett et al. 2012 ). In addition, it is linked to individual learning and memory ( Amin et al. 2015 ). The latter is considered as one of the main predictors of individual academic achievement according to several studies in different settings ( Deary et al. 2007 ; Geary 2011 ; Laidra et al. 2007 ; Monir et al. 2016 ; Verbitskaya et al. 2020 ). Rabbitt and Lowe ( 2000 ) suggest that fluid intelligence is altered in the ageing process, while crystallised intelligence remains stable.

The study of intelligence expanded, and Gardner ( 1985 ) proposed an alternative and a critique of the general intelligence approach by elaborating the theory of multiple intelligences. He proposed the existence of several independent intelligences that interact and mutually enhance each other, such as linguistic, logical–mathematical, spatial, kinaesthetic bodily and others. Thus, most students possess more than one. However, Singh et al. ( 2017 ) report that only logical–mathematical, spatial and musical intelligence are related to IQ. These results are consistent with the research of Castejon et al. ( 2010 ) and Visser et al. ( 2006 ), who reported a strong relationship between cognitive component intelligences and general intelligence.

Sternberg ( 1985 ) elaborated the Triarchic Theory of intelligence, establishing three categories within it: competency, experiential and contextual. Thus, the acquisition and storage of information, the ability to encode, combine and compare that information and finally the adaptation of information to context were involved. He expanded on this theory and called it successful intelligence, which combined ability, exploitation, adaptation, creativity, etc. It is about being able to solve problems, and depending on the way it is done, analytical intelligence (both familiar and abstract problems), creative intelligence (formulating ideas, problems of a novel nature) and practical intelligence (applying ideas and analysis effectively) will appear ( Sternberg et al. 2010 ).

The theories of Gardner ( 1985 ) and Sternberg ( 1985 ) were fundamental for the emergence of the theory of emotional intelligence since these ideas underlay the new concept that would germinate in the theories of Salovey and Mayer ( 1990 ), but it would be Goleman ( 1996 ) who popularised it by stating that emotional intelligence consists of a series of skills such as discovering, recognising and managing emotions and feelings ( Goleman 1999 ). The relevant role of emotional aspects in academic results is evident in previous studies such as the meta-analysis conducted by Molero-Puertas et al. ( 2020 ), which concludes with a significant effect size between emotional intelligence and academic performance. Other research suggests that this variable is a good predictor of academic achievement at different educational stages and even indicates that it is second only to general intelligence ( MacCann et al. 2020 ; Perera and DiGiacomo 2013 ; Sanchez-Ruiz et al. 2013 ).

Finally, implicit intelligence, regarded as the self-perception of intelligence grounded in everyday experience, is a key variable for understanding academic performance ( Enea-Drapeau et al. 2017 ). Since this includes a component of expectation as cognitive self-representation, some authors point out that the relationship is especially direct in the early years and concerning specific performance areas rather than global performance ( Dinger et al. 2013 ; Geary 2011 ; Priess-Groben and Hyde 2017 ; Wigfield et al. 2016 ); other authors argue that implicit intelligence is a good predictor for academic performance in maths ( Kriegbaum et al. 2015 ; Steinmayr and Spinath 2009 ); for Steinmayr et al. ( 2019a ) and Lotz et al. ( 2018 ), this predictive value extends over all areas, as confidence in one’s own abilities can be a more important variable than cognitive abilities in the analysis of academic performance. In this sense, implicit theories are presented as definitions, or theories that scientists have about some phenomena ( Sternberg 1985 ). Precisely in these beliefs lies the importance of understanding people’s implicit theories. This is important because these beliefs guide people’s attitudes and behaviours and, as discussed in various theories of the development of talent and intelligence, intelligence is not composed of a single factor but is multidimensional, with contextual, creative and motivational aspects related to people’s behaviours intervening in its conception. The theory of social cognition indicates that beliefs determine attitudes and willingness to engage in certain behaviours ( Pintrich 2002 ). Undoubtedly, these aspects mean that implicit intelligence must be taken into account in relation to academic performance and learning.

With regard to academic performance, its prediction has been a relevant topic for a long time and different variables have been analysed to help explain the academic results of schoolchildren. Different research has related it to individual characteristics of basic cognitive processes such as processing speed, working memory, fluid intelligence, etc. ( El Jaziz et al. 2020 ; Kiuru et al. 2012 ; Kuncel et al. 2004 ; Richardson et al. 2012 ; Sternberg et al. 2001 ). However, academic performance as a product of learning serves as an indicator of the level of learning ( Alquichire R and Arrieta R 2018 ). For Ariza et al. ( 2018 ), it is nothing more than a measure of what students have learned as a result of an educational process. He defines it as the ability to respond to a series of educational stimuli, which in turn is interpreted on the basis of the established objectives.

In view of previous research, it is not new that measures of general intelligence predict academic performance ( Deary et al. 2007 ; Quílez-Robres et al. 2021b ; Sternberg 2019 ). Systematic study has resulted in the predictive value of intelligence in the educational world and has pointed to significant correlations with different variables, but there is also some variation depending on the educational stage analysed ( Sternberg et al. 2001 ). While agreeing that intelligence is one of the most important variables in academic performance as it has a direct impact on learning ( González et al. 2008 ), it should be noted that it does not behave uniformly, as the correlation between intelligence and academic performance decreases when the student reaches the university stage ( Ren et al. 2015 ).

If the aim is to increase the predictive value of the different measures of intelligence, one possibility is to broaden the concept of intelligence itself. A review of the scientific literature shows that there are no studies that integrate the different types of intelligence theorised in reference to academic achievement. This meta-analysis aims to analyse the relationship between different types of intelligence and academic performance from a meta-analytical perspective by reviewing the scientific literature with a broad conception of the concept of intelligence, taking into account the studies that indicate that there is no single way of understanding and defining this construct.

A research registry protocol ( Figure 1 ) was established following the Cochrane systematic review manual in Higgins and Green ( 2011 ) and PRISMA ( 2015 ). Inclusion criteria were determined using the specifications set out by Ausina and Sánchez-Meca ( 2015 ) and Moreau and Gamble ( 2020 ): (a) Research methodology: quantitative, correlational, longitudinal, cross-sectional and clinical. (b) Time frame: 2000–2020. (c) Methodological rigour: studies indexed in prestigious rankings (Scimago Journal and Country Rank). (d) Measuring instruments: psychometric tests rated in academic publications and in accordance with the culture of the sample. (e) Language: English.

Flowchart of search methodology.

The exclusion criteria were established according to the manuals of Ausina and Sánchez-Meca ( 2015 ) and Moreau and Gamble ( 2020 ): (a) Adult population with previous disorders or pathologies, including, however, research in which there were control groups without pathologies. (b) The appearance of imprecise, poorly defined data, unclear methodology or with indications of non-compliance with ethical principles, as well as statistical or psychometric errors in the measurement of the tests, following the indications of Hunter and Schmidt ( 2004 ) and Friese and Frankenbach ( 2020 ).

The search strategy was carried out using the criteria of Botella and Gambara ( 2002 ), Ausina and Sánchez-Meca ( 2015 ) and PRISMA ( 2015 ). Three databases were used: Psycoinfo, Pubmed and Science Direct, and research was performed in February 2021. The Boleean action was “academic achievement” and “intelligence” in the range 2000–2020.

Eligibility criteria for sample selection were defined according to the Cochrane systematic review manual in Higgins and Green ( 2011 ) and PRISMA ( 2015 ). It should be noted that manual coding was carried out by reviewing each article returned by Boolean actions according to the inclusion and exclusion criteria. Firstly, the abstract was screened so that only those that dealt with the subject of the study were selected. On the other hand, the criteria of methodological rigour and measurement instruments led to the exclusion of a significant percentage of the research. This was due to the absence of standardised instruments or the incorrect measurement of the study parameters according to the pre-established psychometric test.

The transformation of all means to Fisher Z ( Martín-Andrés and Luna del Castillo 2004 ), the execution of the relevant analyses (model comparison and meta-regression), the study of heterogeneity, the performance of the Eggers test for publication bias and the obtaining of figures were carried out using the CMA statistical software.

3.1. Demographic Description

In recent years (2000–2020), the relationship between types of intelligence and academic performance at different educational stages has been studied in depth. In total, the meta-analysis ( Table 1 ) consists of 27 studies with k = 47 samples from Europe, Asia, Africa, America and Oceania. According to Bonett ’s ( 2006 ) criteria, the sample k = 47 exceeds the minimum required to avoid distortion of the upper confidence limit. On the other hand, heterogeneity is evident in the sample sizes, with the smallest sample size being 81 participants and the largest 4036 participants.

Sociodemographic data.

The total sample is made up of 42,061 participants, 47.16% of whom are male and 48.99% female. In this sense, it is necessary to clarify that the two studies do not provide data on the sex of their participants. The average age of the participants is 16.45 years, although some studies did not report a specific average age, but rather a range of years or school years, making it necessary to take the arithmetic mean to be able to manage the data quantitatively.

In terms of culture, social anthropology points to the need to attend to cultural diversity ( Molano 2007 ). In this study, 30.13% are Asian (China, Indonesia and Malaysia), 4.73% are Central European (Germany, UK), 37.37% are Eastern European (Russia), 6.01% are Northern European (Norway and Finland), 2.32% are North African (Morocco and Egypt), 18.83% are American (USA and Barbados) and 0.57% are from Oceania (Australia).

3.2. Statistical Analysis

The aim of this meta-analysis is to study the relationship between type of intelligence and student achievement, but encompassing different educational stages and different contexts. To this end, 108 effect sizes were coded, taking as a reference the data based on Pearson’s r and their subsequent treatment using the CMA statistical programme.

Figure 2 (forest plot) shows the effect size with a 99% confidence interval (0.302–0.428, p = 0.001) for the different studies, the effect size being r = 0.367, p = 0.001. In other words, a moderate level of correlation is obtained according to Cohen between the intelligence presented by the students and academic performance. The ethical criteria set out by Moreau and Gamble ( 2020 ) are followed when exposing all the conversions, opting for a policy of “open materials”.

Forest Plot.

On the other hand, it is crucial to study the heterogeneity of the sample according to Cochrane in Higgins and Green ( 2011 ). The Q statistic of Dersimonian and Laird ( 1986 ) (Q = 2478.71, df = 46, p < 0.0001) describes a high variability, i.e., the homogeneity hypothesis is rejected. The statistic I2 = 98.144% explains the percentage of variability resulting from heterogeneity and not from chance. In other words, the sample is highly heterogeneous in its statistical nature ( Higgins et al. 2013 ). Consistently, the Random model or random effects model is applied ( Bonett 2006 ; Martín-Andrés and Luna del Castillo 2004 ). Although the inclusion and exclusion criteria contemplate the reliability and methodological and psychometric quality of the research, the Egg’s test with 99% reliability was carried out to study the effect of bias ( Botella and Gambara 2002 ; Ausina and Sánchez-Meca 2015 ). The results of the test show the inexistence of publication bias with a 99% confidence interval ( p -value 1 tailed = 0.07; p -value 2 tailed = 0.15) ( Egger et al. 1997 ). The standard error value (SE = 2.04) reaffirms the absence of bias, as it is very close to the regression line ( Martín-Andrés and Luna del Castillo 2004 ).

The diversity shown in the Q and I2 statistics could be a sign of extreme data; however, the tight confidence interval (0.302–0.428, p = 0.001) limits this heterogeneity. These results are consistent with the Funnel Plot graph ( Figure 3 ) where the variability and heterogeneity of the sample are reaffirmed. This situation reiterates the diversity of studies, as concluded by the Egger test, without any bias effect. However, it should be noted that the apparent variability could be affected by the transformation to Fisher Z -values since x-values >0.5 tend to be more distorted on the T-Student curve than, in comparison, on the normal curve, although this transformation is accepted by the scientific community for meta-analysis methodology ( Martín-Andrés and Luna del Castillo 2004 ).

Funnel plot and standard error.

3.3. Moderating Variables and Meta-Regression Analysis

The state-of-the-art research shows the existence of moderating factors, which is why it is considered necessary to establish the study of seven moderating variables: type of intelligence, type of performance, age, country, male sex, female sex and geographical distribution. The objective pursued through the use of both techniques is to statistically determine the reason for such heterogeneity ( Ausina and Sánchez-Meca 2015 ; Jak and Cheung 2020 ). In this way, a comparison of models is established (see Table 2 ) by generating seven models: (1) type of intelligence; (2) type of performance; (3) age; (4) country; (5) male gender; (6) female gender; (7) geographical distribution.

Comparison of models: Random effects (MM), Z distribution, Fisher’s Z.

The first model, which specifies the type of intelligence, explains 35% of academic performance, with an efficiency level of over 99%, although, as model 2 shows, the type of performance has no effect. In other words, it is intelligence that determines student academic performance and success, but doing well in these subjects does not seem to affect intelligence overall. On the other hand, the models of age, gender and geographical distribution do not explain the relationship between the two factors to any extent. However, there are important differences between countries, which may be explained by diversity in the education system. Association with a given nation accounts for 45% of the variability in the sample ( Table 2 ).

It is therefore necessary to study in greater depth the type of intelligence that seems to determine academic performance. For this reason, a meta-regression ( Table 3 ) is carried out in which it is evident that general intelligence (Z = 2.00, p = 0.04) and implicit intelligence (Z = 3.69, p = 0.00) are the ones that stand out, showing a clear difference.

Meta-regression of model 1: Intelligence.

As far as the different countries are concerned, significant differences are found in the comparison models. Therefore, it is necessary to perform a meta-regression (see Table 4 ) that points out the differences between education systems. In this case, the countries that differ from the sample are China, Indonesia and the UK (United Kingdom).

Meta-regression of model 2: countries.

4. Discussion

Given that the review of the scientific literature indicates that there is no single way of understanding, defining and analysing the construct of intelligence, this meta-analysis analyses the relationship between intelligence and academic performance in terms of the different types of intelligence studied in previous research, as well as the existence of models of moderating variables that clarify their predictive nature. Therefore, effect size, type of intelligence (general, crystallised, fluid, implicit, emotional, etc.), age, gender, country of residence or geographical area are of interest for this study. Of all these variables, effect size, general intelligence, implicit intelligence (R 2 = 0.35; p < 0.001) and country of residence (R 2 = 0.45; p < 0.001) are those that appear to be relevant and significant.

From the results obtained, a number of factors stand out, such as the relationship between academic performance and intelligence with a moderate effect size (0.367; significance < 0.001). Previous research addressing the interrelations between intelligence and academic performance indicates that it is the most stable and powerful predictor of school performance (r = 0.5) ( Geary 2011 ; Laidra et al. 2007 ; Luo et al. 2006 ; Rodic et al. 2015 ). These results are corroborated in the meta-analysis of Cortés Pascual et al. ( 2019 ) who equate it with that obtained for executive functions. They point out that intelligence is decisive for new learning and, on the contrary, executive functions are primordial for repetitive and competence-focused learning and also show their relationship in different educational disciplines.

Another noteworthy element of the research is that when analysing moderating variables and comparing models, it is found that intelligence determines that the relationship with academic performance is unidirectional. That is, intelligence is a good predictor of academic achievement, but not the other way around, so the predictive model of intelligence type explains 35% of the variance. Consistent with this result, Buckle et al. ( 2005 ) assigned it a predictive power of 26%. This is in line with previous research findings that intelligence is the best predictor of academic success ( Blankson et al. 2019 ; Erath et al. 2015 ; Li et al. 2017 ; Quílez-Robres et al. 2021a ; Ren et al. 2015 ; Rhodes et al. 2017 ; Tikhomirova et al. 2020 ). However, most studies have related it to the cognitive dimension ( Castejon et al. 2010 ; Visser et al. 2006 ), marginalising the behavioural and emotional aspects ( Gioia et al. 2017 ). Therefore, it is necessary to consider other facets of intelligence, as they are nothing more than different capacities that complement each other ( Catell 1963 ).

From the meta-regression of the intelligence model, general and implicit intelligence emerge with significance ( p < 0.05 and p < 0.01). Implicit intelligence is decisive in school outcomes, as the beliefs that are elaborated about one’s own intelligence and the nature of intelligence guide student behaviours towards achieving success or failure at school ( Chen and Tutwiler 2017 ; Lotz et al. 2018 ; Steinmayr et al. 2019b ). Thus, it is considered relevant for its efficacy in considering that cognitive ability is not a fixed trait but has an adaptive quality that gives it incremental strength. This malleability performs a protective function against school failure, as there is confidence in one’s own abilities. However, it seems that this incremental capacity decreases with age ( Chen and Tutwiler 2017 ). In the same line of research, Kornilova et al. ( 2009 ) found that implicit intelligence predicts general intelligence, as by adopting learning goals and increasing their competence, students overcome setbacks and seek new challenges. It should also be noted that both general and implicit intelligence show an indirect effect with academic success through other variables such as motivation or executive functions ( Aditomo 2015 ). Furthermore, it should be noted that general intelligence has traditionally been broken down into fluid and crystallised intelligence. “Crystallised” intelligence has been considered one of the most significant predictors of individual achievement in different contexts, age ranges and educational conditions ( Deary et al. 2007 ; Nisbett et al. 2012 ; Verbitskaya et al. 2020 ), and “fluid” intelligence has been found to be a better predictor of processing speed tests ( Luo et al. 2006 ) and mathematics performance ( Blankson et al. 2019 ; Sarver et al. 2012 ).

Neither emotional intelligence nor the different types of multiple intelligences show remarkable values in this research. In this regard, the scientific literature is not conclusive. Some studies find that emotional intelligence occupies a pre-eminent position behind general or global intelligence ( MacCann et al. 2020 ; Perera and DiGiacomo 2013 ) and explain that this type of intelligence is related to academic performance due to its importance in promoting adaptive behaviours ( Chew et al. 2013 ; Fayombo 2012 ; Usán Supervía and Quílez Robres 2021 ). The perception of positive interpersonal and intrapersonal emotional intelligence substantially explains academic success, as it comprises learner ability to control, regulate and manage the demands of the academic context ( Cheshire et al. 2015 ; Chew et al. 2013 ; Kornilova et al. 2018 ; Okwuduba et al. 2021 ; Romero et al. 2014 ). However, the research that studies this claim presents mixed results since some authors such as Engin ( 2017 ) or Zhoc et al. ( 2018 ) did not observe associations between academic performance and emotional intelligence. On the other hand, and within the multiple intelligences, musical intelligence has been related to academic performance, especially through cross-sectional data, from which it is difficult to infer a generalisation of cause and effect with respect to school achievement ( Müllensiefen et al. 2015 ; Schellenberg 2011 ). This is despite the fact that Castejon et al. ( 2010 ) and Visser et al. ( 2006 ) point to the existence of a relationship between some of the multiple intelligences and general intelligence due to their cognitive component.

Concerning the type of “perfomance”, comparison analysis model showed its non-significance. However, it is necessary to point out that the performance types are not homogeneous in the meta-sample. There is a large amount of general performance, but there are hardly any cases of music or mathematics. Despite its non-significance, investigation of this aspect in further research is considered necessary, and nevertheless, there are studies that advocate the importance of this variable.

As for gender and age, they are not moderating variables, perhaps influenced by the type of assessment of these variables and the different theoretical concepts of greater or lesser importance assigned to the relationship between them. On the other hand, there are difficulties in predicting the role of gender and age in implicit intelligence ( Diseth et al. 2014 ), but Robins and Trzesniewski ( 2005 ) point out that there is a strong relationship in favour of girls and at an older age. These results can be related to emotional intelligence and higher perceived self-efficacy ( King et al. 2012 ).

As noted above, the country of residence model is the moderating variable that explains 45% of the variance, increasing the predictive power of the intelligence type model. These results are consistent with previous research pointing to the importance of adaptation to different contexts ( Deary et al. 2007 ; Verbitskaya et al. 2020 ) or those indicating that the relationship between intelligence and academic performance was the result of education and the culture in which one was immersed ( Nisbett et al. 2012 ; Plomin and Deary 2015 ; Rodic et al. 2015 ). In his theories, Sternberg, for example, pointed out the importance of adaptation to the context of different skills and abilities, as well as of the differences originating in the beliefs of one’s own abilities in their contribution to academic achievement as a function of the cultural environment that generate individual profiles with different strengths and weaknesses ( Sternberg 2019 ; Sternberg et al. 2001 ). Ultimately, intelligence is related to social competence ( Sternberg 1985 ).

When analysing the meta-regression across countries, three countries are significant: Indonesia, the United Kingdom (UK) and China. Indonesia is considered a very deterministic culture (if you are not very smart, you do not pass) ( Aditomo 2015 ). On the other hand, the United Kingdom (UK) as a model of the Anglo-Saxon education system associates intelligence with linguistic ability and problem-solving skills ( Sternberg 1985 ). Finally, in China, authoritarian filial piety beliefs are associated with an entity view of intelligence, which impairs the students’ academic performance ( Chen and Wong 2014 ). Cultural views of motivational processes can shed light on the ways in which motivational beliefs develop as a product of cultural or socialisation processes, which, in turn, contribute to or determine the students’ academic success ( Chen and Wong 2014 ; Li et al. 2017 ). These differences by country of origin are likely to point to the meanings attributed to intelligence by different cultural groups. There are indications that individuals from Western countries attach a much broader meaning to the concept of intelligence (skills, context, etc.) and, therefore, when studying subjects from non-Western countries, consideration should be given to using specific domains that provide greater certainty to the results, always bearing in mind that the mindset about intelligence and academic ability is very different ( Aditomo 2015 ). As Carroll ( 1992 ) points out, intelligence is a concept within the mind of a society and personal references are those of each culture where individuals are immersed. Some cultures such as the Asian ones continue to use teaching–learning methods based on cognitive aspects such as memory and one’s own intelligence, while the European and Anglo-Saxon models are based on the development of competence through social interaction ( Quílez-Robres et al. 2021a ).

Other reason may be due to different factors such as, for example, the statistical weight of the samples, or others related to cultural elements such as different understandings of academic performance and different assessments of different types of intelligence.

Furthermore, following Serpell ( 2000 ), culture can be approached from three perspectives: culture as a language, culture as a womb, and culture as a forum. According to the language perspective, culture would constitute a distinct system of meanings in the mind within which the concept of intelligence would be embedded. According to the womb perspective, human cultures create environments that nurture personal growth and stimulate the development of human intelligence. Finally, the forum view, which is based on the interaction of members of a community organising aspects of education and constructing new meanings about intelligence, proposes research on cognitive development as a function of culture.

On the other hand, Sternberg and Grigorenko ( 2004 ) indicate that intelligence cannot be understood completely outside of cultural control or influence. There are behaviours that are considered intelligent in some cultures, and those same behaviours are considered unintelligent in other cultures. Furthermore, each culture has implicit (folk) theories of intelligence, and therefore the aspects that fall under this concept vary from culture to culture. In this sense, the three influential cultures in this study belong to two different cultural approaches: individualistic (UK) versus collectivistic (China and Indonesia). Moreover, these countries have different ways of understanding academic performance and attach different degrees of importance to intelligence in academic, social and occupational performance ( Quílez-Robres et al. 2021b ).

5. Conclusions

This research was conducted to identify the ways in which different aspects of student intelligence contribute to differences in academic performance. Of the seven models studied, the country of residence model was found to be the most important predictor of academic performance, explaining 45% of the variance, followed by the type of intelligence model, which explains 35% of the variance. The latter model highlights the importance of general intelligence and implicit intelligence for student grades in academic subjects. The results therefore extend knowledge about the role of intelligence for academic achievement. Implicit intelligence scores better in relation to academic achievement than global intelligence, highlighting the importance of one’s beliefs in one’s own abilities. Students with similar intelligence scores, with identical values and the same prior attainment will see improved academic outcomes by believing in their own competencies and abilities ( Steinmayr et al. 2019a ). If one concludes that academic performance is determined by a multitude of variables including psychological factors that influence student response to overcome setbacks, the evidence points to intelligence as a predictor of success, but also, as this research shows, to a positive mindset in relation to one’s own intelligence and academic abilities. This positive mindset will also be established by the context in which their academic life takes place, i.e., society, beliefs, values, education system, etc. ( Aditomo 2015 ; Hong et al. 1999 ). Therefore, the results of this study point the way to implement interventions aimed at improving the students’ own beliefs about their subject-specific mastery skills.

Finally, we conclude with the need to expand the study in order to limit the term intelligence. What would its general structure be, and how do the different types of intelligence add significance to the general and traditional concept? What conceptual divergences exist between the different theories? Do all these concepts have the same impact on new or repeated learning, on general and specific?

Acknowledgments

The authors would like to thank the University of Zaragoza for their support in this research.

Funding Statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author Contributions

Conceptualization, A.Q.-R. and R.L.-B.; methodology, A.Q.-R.; software, R.L.-B.; validation, R.C.-L., P.U. and C.S.; formal analysis, R.L.-B.; investigation, R.L.-B.; resources, A.Q.-R.; data curation, A.Q.-R.; writing—original draft preparation, R.L.-B.; writing—review and editing, A.Q.-R.; visualization, R.C.-L.; supervision, C.S.; project administration, P.U. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Fast and Accurate LSTM Meta-modeling of TNF-induced Tumor Resistance In Vitro

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Multi-level, hybrid models and simulations are essential to enable predictions and hypothesis generation in systems biology research. However, the computational complexity of these models poses a bottleneck, limiting the applicability of methodologies relying on large number of simulations, such as the Optimization via Simulation (OvS) of complex biological processes. Meta-models based on approximate surrogate models simplify multi-level simulations, maintaining accuracy while reducing computational costs. Among Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM) networks are well suited to handle sequential data, which often characterizes biological simulations. This paper presents an LSTM-based surrogate modeling approach for multi-level simulations of complex biological processes. The approach accurately infers simulation evolution from any state. Validation relies on the simulation of Tumor Necrosis Factor (TNF) administration to a 3T3 mouse fibroblasts tumor spheroid based on PhysiBoSS 2.0, a hybrid agent-based multi-level modeling framework. Results show that the proposed LSTM meta-model is accurate and fast. In fact, it infers simulated behavior with an average relative error of 7.5%. Moreover, it is at least five orders of magnitude faster. Even considering the cost of training, this approach provides a faster, more accurate, and reusable surrogate of multi-scale simulations in computationally complex tasks, such as model-based OvS of biological processes.

Competing Interest Statement

The authors have declared no competing interest.

https://github.com/smilies-polito/LSTMeta-TNF

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