Revising the JBI quantitative critical appraisal tools to improve their applicability: an overview of methods and the development process
Bibliographic record
Abstract
JBI offers a suite of critical appraisal instruments that are freely available to systematic reviewers and researchers investigating the methodological limitations of primary research studies. The JBI instruments are designed to be study-specific and are presented as questions in a checklist. The JBI instruments have existed in a checklist-style format for approximately 20 years; however, as the field of research synthesis expands, many of the tools offered by JBI have become outdated. The JBI critical appraisal tools for quantitative studies (eg, randomized controlled trials, quasi-experimental studies) must be updated to reflect the current methodologies in this field. Cognizant of this and the recent developments in risk-of-bias science, the JBI Effectiveness Methodology Group was tasked with updating the current quantitative critical appraisal instruments. This paper details the methods and rationale that the JBI Effectiveness Methodology Group followed when updating the JBI critical appraisal instruments for quantitative study designs. We detail the key changes made to the tools and highlight how these changes reflect current methodological developments in this field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.560 | 0.794 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.046 | 0.035 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.005 | 0.017 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".