Cochrane Rapid Reviews Methods Group offers evidence-informed guidance to conduct rapid reviews
Bibliographic record
Abstract
OBJECTIVES: To develop methods guidance to support the conduct of rapid reviews (RRs) produced within Cochrane and beyond, in response to requests for timely evidence syntheses for decision-making purposes including urgent health issues of high priority. STUDY DESIGN AND SETTING: Interim recommendations were informed by a scoping review of the underlying evidence, primary methods studies conducted, and a survey sent to 119 representatives from 20 Cochrane entities, who were asked to rate and rank RR methods across stages of review conduct. Discussions among those with expertise in RR methods further informed the list of recommendations with accompanying rationales provided. RESULTS: Based on survey results from 63 respondents (53% response rate), 26 RR methods recommendations are presented for which there was a high or moderate level of agreement or scored highest in the absence of such agreement. Where possible, how recommendations align with Cochrane methods guidance for systematic reviews is highlighted. CONCLUSION: The Cochrane Rapid Reviews Methods Group offers new, interim guidance to support the conduct of RRs. Because best practice is limited by the lack of currently available evidence for some RR methods shortcuts taken, this guidance will need to be updated as additional abbreviated methods are evaluated.
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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 | 0.387 | 0.670 |
| Meta-epidemiology (narrow) | 0.007 | 0.012 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.036 | 0.035 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.012 | 0.013 |
| Research integrity | 0.024 | 0.023 |
| Insufficient payload (model declined to judge) | 0.122 | 0.110 |
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".