Rapid reviews methods series: Guidance on team considerations, study selection, data extraction and risk of bias assessment
Bibliographic record
Abstract
This paper is part of a series of methodological guidance from the Cochrane Rapid Reviews Methods Group (RRMG). Rapid reviews (RRs) use modified systematic review (SR) methods to accelerate the review process while maintaining systematic, transparent and reproducible methods to ensure integrity. This paper addresses considerations around the acceleration of study selection, data extraction and risk of bias (RoB) assessment in RRs. If a RR is being undertaken, review teams should consider using one or more of the following methodological shortcuts: screen a proportion (eg, 20%) of records dually at the title/abstract level until sufficient reviewer agreement is achieved, then proceed with single-reviewer screening; use the same approach for full-text screening; conduct single-data extraction only on the most relevant data points and conduct single-RoB assessment on the most important outcomes, with a second person verifying the data extraction and RoB assessment for completeness and correctness. Where available, extract data and RoB assessments from an existing SR that meets the eligibility criteria.
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 | 0.385 | 0.651 |
| Meta-epidemiology (narrow) | 0.006 | 0.011 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.166 | 0.132 |
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".