Rapid review methods more challenging during COVID-19: commentary with a focus on 8 knowledge synthesis steps
Bibliographic record
Abstract
What is new?Key findings•Guidance is available on the conduct of rapid reviews. However, the COVID-19 pandemic has created several unique challenges.•Challenges to the conduct of rapid reviews include the urgency of the request from decision-maker organizations, identification of and access to sources of evidence for inclusion in the rapid reviews, extrapolation of results from indirect evidence, and dissemination of results widely.What this adds to what is known?•There is a need for coordination of efforts internationally to reduce the risk of duplication, and to effectively use global collective evidence synthesis resources.•We outline several methodological challenges to the conduct of rapid reviews that have become apparent during the COVID-19 pandemic using an 8-step framework that follows the knowledge synthesis process.What is the implication and what should change now?•We offer several suggestions to help address the methodological challenges encountered during the conduct of rapid reviews on COVID-19, as well as future research. Key findings•Guidance is available on the conduct of rapid reviews. However, the COVID-19 pandemic has created several unique challenges.•Challenges to the conduct of rapid reviews include the urgency of the request from decision-maker organizations, identification of and access to sources of evidence for inclusion in the rapid reviews, extrapolation of results from indirect evidence, and dissemination of results widely.What this adds to what is known?•There is a need for coordination of efforts internationally to reduce the risk of duplication, and to effectively use global collective evidence synthesis resources.•We outline several methodological challenges to the conduct of rapid reviews that have become apparent during the COVID-19 pandemic using an 8-step framework that follows the knowledge synthesis process.What is the implication and what should change now?•We offer several suggestions to help address the methodological challenges encountered during the conduct of rapid reviews on COVID-19, as well as future research.
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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 | 0.415 | 0.787 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.019 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.026 | 0.038 |
| Open science | 0.015 | 0.013 |
| Research integrity | 0.050 | 0.050 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".