Rapid reviews and the methodological rigor of evidence synthesis: a JBI position statement
Bibliographic record
Abstract
ABSTRACT: The demand for rapid reviews has exploded in recent years. A rapid review is an approach to evidence synthesis that provides timely information to decision-makers (eg, health care planners, providers, policymakers, patients) by simplifying the evidence synthesis process. A rapid review is particularly appealing for urgent decisions. JBI is a world-renowned international collaboration for evidence synthesis and implementation methodologies. The principles for JBI evidence synthesis include comprehensiveness, rigor, transparency, and a focus on applicability to clinical practice. As such, JBI has not yet endorsed a specific approach for rapid reviews. In this paper, we compare rapid reviews versus other types of evidence synthesis, provide a range of rapid evidence products, outline how to appraise the quality of rapid reviews, and present the JBI position on rapid reviews. JBI Collaborating Centers conduct rapid reviews for decision-makers in specific circumstances, such as limited time or funding constraints. A standardized approach is not used for these cases;instead, the evidence synthesis methods are tailored to the needs of the decision-maker. The urgent need to deliver timely evidence to decision-makers poses challenges to JBI's mission to produce high-quality, trustworthy evidence. However, JBI recognizes the value of rapid reviews as part of the evidence synthesis ecosystem. As such, it is recommended that rapid reviews be conducted with the same methodological rigor and transparency expected of JBI reviews. Most importantly, transparency is essential, and the rapid review should clearly report where any simplification in the steps of the evidence synthesis process has been taken.
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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 | 0.735 | 0.852 |
| Meta-epidemiology (narrow) | 0.005 | 0.008 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.063 | 0.030 |
| Open science | 0.021 | 0.027 |
| Research integrity | 0.091 | 0.112 |
| Insufficient payload (model declined to judge) | 0.007 | 0.011 |
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".