A preliminary survey on the influence of rapid health technology assessments
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to obtain information on rapid health technology assessments (HTAs) prepared by members of the International Network of Agencies for Health Technology Assessment (INAHTA). METHODS: A questionnaire was prepared, drawing on earlier INAHTA documents for recording HTA impact. A request for responses was sent to member agencies, seeking information on rapid HTA reports prepared during 2006. RESULTS: Responses were provided on fifteen rapid HTAs, which covered both new and widely distributed technologies. The most common purpose for the HTAs (n = 8) was to inform coverage decisions, but other reasons included capital funding, formulary decisions, referral for treatment, program operation, guideline formulation, influence on routine practice, and indications for further research. All the rapid HTAs were considered by the agencies to have had some influence. The most common indications of influence were consideration by the decision maker, use of the HTA as reference material (both n = 10), and acceptance of recommendations or conclusions (n = 8). CONCLUSIONS: Rapid HTAs are used for a broad range of technologies, to inform several types of decision, and are effective in informing the decision-making process. Supplementation of their findings by further assessments will be appropriate in some cases.
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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 | 0.049 | 0.162 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".