A common policy framework for evidence generation on promising health technologies
Bibliographic record
Abstract
BACKGROUND: Generation of additional evidence may be necessary to access new promising technologies (marketing approval or coverage). Access with evidence generation (AEG) is a more recent concept with regard to coverage than to marketing approval. OBJECTIVES: One aim of Work Package 7 (WP7) Strand A of the European network for Health Technology Assessment (EUnetHTA) was to provide an overview of national AEG mechanisms associated with marketing approvals and funding or coverage decisions. METHODS: A systematic literature review, surveys of WP7 Partners, and consultation of key people were used to obtain information on the AEG mechanisms used by twenty-three countries (twenty European countries, United States, Canada [Ontario], and Australia). RESULTS: Interest in the implementation of AEG policies, particularly at the coverage decision stage, is growing. An overview of national experiences was used to draw up a generally applicable five-step policy framework for AEG mechanisms that comprised (i) a first assessment identifying knowledge gaps; (ii) a decision conditional to evidence generation; (iii) generation of the evidence requested; (iv) re-assessment integrating the new evidence; (v) a revised decision. The critical factors for success that were identified were coordination, methodological guidance, funding, and a regulatory framework. Countries were categorized on the basis of current implementation of the proposed policy framework. CONCLUSIONS: International collaboration is necessary to gather a critical mass of high-quality data quickly and to ensure timely access to new promising technologies. The overview produced by WP7A has led to development of tools to facilitate collaboration on evidence generation.
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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 | 0.638 | 0.539 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.041 | 0.021 |
| Science and technology studies | 0.013 | 0.043 |
| Scholarly communication | 0.054 | 0.049 |
| Open science | 0.022 | 0.043 |
| Research integrity | 0.044 | 0.026 |
| Insufficient payload (model declined to judge) | 0.014 | 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; 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".