RAPID VERSUS FULL SYSTEMATIC REVIEWS: VALIDITY IN CLINICAL PRACTICE?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.172 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it