Key concepts in rapid reviews: an overview
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Rapid reviews have gained popularity as a pragmatic approach to synthesize evidence in a timely manner to inform decision-making in healthcare. This article provides an overview of the key concepts and methodological considerations in conducting rapid reviews, drawing from a series of recently published guidance papers by the Cochrane Rapid Reviews Methods Group. STUDY DESIGN AND SETTING: We discuss the definition, characteristics, and potential applications of rapid reviews and the trade-offs between speed and rigor. We present a practical example of a rapid review and highlight the methodological considerations outlined in the updated Cochrane guidance, including recommendations for literature searching, study selection, data extraction, risk of bias assessment, synthesis, and assessing the certainty of evidence. RESULTS: Rapid reviews can be a valuable tool for evidence-based decision-making, but it is essential to understand their limitations and adhere to methodological standards to ensure their validity and reliability. CONCLUSION: As the demand for rapid evidence synthesis continues to grow, further research is needed to refine and standardize the methods and reporting of rapid reviews.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.296 | 0.600 |
| Meta-epidemiology (narrow) | 0.008 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.051 | 0.052 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".