Advancing knowledge of rapid reviews: an analysis of results, conclusions and recommendations from published review articles examining rapid reviews
Bibliographic record
Abstract
BACKGROUND: Rapid review (RR) products are inherently appealing as they are intended to be less time-consuming and resource-intensive than traditional systematic reviews (SRs); however, there is concern about the rigor of methods and reliability of results. In 2013 to 2014, a workgroup comprising representatives from the Agency for Healthcare Research and Quality's Evidence-based Practice Center Program conducted a formal evaluation of RRs. This paper summarizes results, conclusions, and recommendations from published review articles examining RRs. METHODS: A systematic literature search was conducted and publications were screened independently by two reviewers. Twelve review articles about RRs were identified. One investigator extracted data about RR methods and how they compared with standard SRs. A narrative summary is presented. RESULTS: A cross-comparison of review articles revealed the following: 1) ambiguous definitions of RRs, 2) varying timeframes to complete RRs ranging from 1 to 12 months, 3) limited scope of RR questions, and 4) significant heterogeneity between RR methods. CONCLUSIONS: RR definitions, methods, and applications vary substantially. Published review articles suggest that RRs should not be viewed as a substitute for a standard SR, although they have unique value for decision-makers. Recommendations for RR producers include transparency of methods used and the development of reporting standards.
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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 | 0.447 | 0.740 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.018 |
| Bibliometrics | 0.082 | 0.061 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".