RAPID REVIEW: AN EMERGING APPROACH TO EVIDENCE SYNTHESIS IN HEALTH TECHNOLOGY ASSESSMENT
Bibliographic record
Abstract
BACKGROUND: Increasingly, healthcare decision makers demand quality evidence in a short timeframe to support urgent and emergent decisions related to procurement, clinical practice, and policy. Health technology assessment (HTA) producers are responding by developing innovative approaches to evidence synthesis that can be executed more quickly than traditional systematic review. These approaches, and the broader implications they bring to bear on health decision making and policy development, however, are generally neither well-understood nor well-described. This study intends to contribute to an emerging literature around methodological approaches to rapid review in HTA by outlining those developed and implemented by the Canadian Agency for Drugs and Technologies in Health (CADTH). METHODS: Since 2005, CADTH has developed and implemented a rapid review approach that synthesizes evidence to support informed healthcare decisions and policy. Rapid Response reports are tailored to the identified needs of Canadian health decision makers, representing a range of options with regard to depth, breadth, and time-to-delivery. RESULTS: Preliminary observations indicate that CADTH's approach to rapid evidence review is generally well-received by Canadian health decision makers; real-world case studies provide pragmatic examples of how health decision makers have used Rapid Response reports to support evidence-informed health decisions across Canada. CONCLUSIONS: Rapid review is becoming an increasingly important approach to evidence synthesis, both within and external to the field of HTA. Transparent reporting of the methods used to develop rapid review products will be critical to the assessment of their relevance, utility and effects in a range of contexts.
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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 | 0.704 | 0.825 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.014 |
| Bibliometrics | 0.053 | 0.046 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.049 | 0.028 |
| Open science | 0.015 | 0.025 |
| Research integrity | 0.021 | 0.021 |
| Insufficient payload (model declined to judge) | 0.012 | 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".