The revised JBI critical appraisal tool for the assessment of risk of bias for randomized controlled trials
Bibliographic record
Abstract
JBI recently began the process of updating and revising its suite of critical appraisal tools to ensure that these tools remain compatible with recent developments within risk of bias science. Following a rigorous development process led by the JBI Effectiveness Methodology Group, this paper presents the revised critical appraisal tool for the assessment of risk of bias for randomized controlled trials. This paper also presents practical guidance on how the questions of this tool are to be interpreted and applied by systematic reviewers, while providing topical examples. We also discuss the major changes made to this tool compared to the previous version and justification for why these changes facilitate best-practice methodologies 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 | 0.586 | 0.842 |
| Meta-epidemiology (narrow) | 0.008 | 0.009 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.048 | 0.037 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.013 | 0.018 |
| Research integrity | 0.014 | 0.042 |
| Insufficient payload (model declined to judge) | 0.021 | 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".