The revised JBI critical appraisal tool for the assessment of risk of bias for cohort studies
Bibliographic record
Abstract
Cohort studies are a robust analytical observational study design that explore the difference in outcomes between two cohorts, differentiated by their exposure status. Despite being observational in nature, they are often included in systematic reviews of effectiveness, particularly when randomized controlled trials are limited or not feasible. Like all studies included in a systematic review, cohort studies must undergo a critical appraisal process to assess the extent to which a study has considered potential bias in its design, conduct, or analysis. Critical appraisal tools facilitate this evaluation. This paper introduces the revised critical appraisal tool for cohort studies, completed by the JBI Effectiveness Methodology Group, who are currently revising the suite of JBI critical appraisal tools for quantitative study designs. The revised tool responds to updates in methodological guidance from the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) Working Group and reporting guidance from PRISMA 2020, providing a robust framework for evaluating risk of bias in a cohort study. Transparent and rigorous assessment using this tool will assist reviewers in understanding the validity and relevance of the results and conclusions drawn from a systematic review that includes cohort studies. This may contribute to better evidence-based decision-making in health care. This paper discusses the key changes made to the tool, outlines justifications for these changes, and provides practical guidance on how this tool should be interpreted and applied by systematic reviewers.
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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 | 0.532 | 0.823 |
| Meta-epidemiology (narrow) | 0.008 | 0.008 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.040 | 0.034 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.012 | 0.016 |
| Research integrity | 0.012 | 0.024 |
| Insufficient payload (model declined to judge) | 0.041 | 0.015 |
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".