Rapid reviews of medical tests used many similar methods to systematic reviews but key items were rarely reported: a scoping review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Rapid reviews provide an efficient alternative to standard systematic reviews in response to a high priority or urgent need. Although rapid reviews of interventions have been extensively evaluated, little is known about the characteristics of rapid reviews of diagnostic evidence. STUDY DESIGN AND SETTING: We performed a scoping review for rapid reviews of medical tests published from 2013 to 2018. We extracted information on review characteristics and methods used to assess the evidence. RESULTS: We identified 191 rapid reviews. All reviews were developed within a short time (less than 12 months) and were relatively concise (less than 10 pages). The reviews involved multiple index tests (44%), multiple outcomes (88%), and several test applications (29%). Well-known methodological tailoring strategies were infrequently used. Although reporting of several key features was limited, we found that, in general, rapid reviews have similar characteristics to broader knowledge syntheses. CONCLUSION: Our scoping review is the first to describe the characteristics and methods of rapid reviews of diagnostic evidence. Future research should identify the most appropriate methods for performing rapid reviews of medical tests. Standards for reporting of rapid reviews are needed.
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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 | 0.362 | 0.678 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.022 |
| Bibliometrics | 0.075 | 0.085 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".