Updated recommendations for the Cochrane rapid review methods guidance for rapid reviews of effectiveness
Bibliographic record
Abstract
This article provides updated guidance on methods for conducting rapid reviews of effectiveness, targeted at Cochrane and other stakeholders interested in the methodology of rapid reviews. The guidance, developed by the Cochrane Rapid Reviews Methods Group, builds upon previous interim guidance, and incorporates changes based on an evaluation of its application, a scope of the literature on rapid review methodology, and input from a diverse group of experts in rapid review methods. The guidance consists of 24 specific recommendations supporting the conduct of rapid reviews, applicable both within and outside Cochrane. It underscores the importance of considering the appropriateness of undertaking rapid reviews and advocates for a tailored, iterative approach to each review. Key defining features of rapid reviews, such as restricted methods, how the dimension of timelines factors into rapid reviews, and the involvement of knowledge users (eg, patient and public partners, healthcare providers, policy makers), are outlined. The paper presents a definition of a Cochrane rapid review and additional considerations for rapid reviews of effectiveness to enhance the efficiency of the review process. In conclusion, the Cochrane Rapid Review Methods Group’s updated guidance, complemented by examples, seeks to guide methodological decisions in the design and conduct of rapid reviews, facilitating timely decision making in healthcare.
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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 | 0.170 | 0.528 |
| Meta-epidemiology (narrow) | 0.005 | 0.009 |
| Meta-epidemiology (broad) | 0.012 | 0.019 |
| Bibliometrics | 0.034 | 0.039 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.013 | 0.008 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.113 | 0.057 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".