RAPID VERSUS FULL SYSTEMATIC REVIEWS: VALIDITY IN CLINICAL PRACTICE?
Bibliographic record
Abstract
INTRODUCTION: Rapid reviews are being produced with greater frequency by health technology assessment (HTA) agencies in response to increased pressure from end-user clinicians and policy-makers for rapid, evidence-based advice on health-care technologies. This comparative study examines the differences in methodologies and essential conclusions between rapid and full reviews on the same topic, with the aim of determining the validity of rapid reviews in the clinical context and making recommendations for their future application. METHODS: Rapid reviews were located by Internet searching of international HTA agency websites, with any ambiguities resolved by further communication with the agencies. Comparator full systematic reviews were identified using the University of York Centre for Reviews and Dissemination HTA database. Data on a number of review components were extracted using standardized data extraction tables, then analysed and reported narratively. RESULTS: Axiomatic differences between all the rapid and full reviews were identified; however, the essential conclusions of the rapid and full reviews did not differ extensively across the topics. For each of the four topics examined, it was clear that the scope of the rapid reviews was substantially narrower than that of full reviews. The methodology underpinning the rapid reviews was often inadequately described. CONCLUSIONS: Rapid reviews do not adhere to any single validated methodology. They frequently provide adequate advice on which to base clinical and policy decisions; however, their scope is limited, which may compromise their appropriateness for evaluating technologies in certain circumstances.
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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 | 0.746 | 0.930 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.023 | 0.036 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".