Interactions between health technology assessment, coverage, and regulatory processes: Emerging issues, goals, and opportunities
Bibliographic record
Abstract
BACKGROUND: The relationship between regulatory approval on the one hand and health technology assessment (HTA) and coverage on the other is receiving growing attention. Those responsible for regulatory approval, HTA, and coverage have different missions and their information requirements reflect these. There is nonetheless an increasingly popular view that improved communication and coordination between these functions could allow them all to be undertaken effectively with a lower overall burden of evidence requirements, thus speeding patient access to new products and reducing unnecessary barriers to innovation. This study summarizes the main points emerging from a recent discussion of this topic at the HTAi Policy Forum. RESULTS AND CONCLUSIONS: After considering the roles of the various bodies, stakeholder perspectives and some current practical initiatives, those present at the Forum meeting discussed possible goals and challenges for improved interactions-in general and at specific stages of the product development life cycle. Opportunities for progress were seen in: continuing the dialogue to promote understanding and interaction between the different bodies and stakeholders; working to align scientific advice for manufacturers on the design and data requirements of pre- and post-marketing evaluation of products (specifically phase 2/3 and phase 4 trials for drugs); and extending the current dialogue to include discussion of product development to address unmet health needs.
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.
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 | 0.241 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.037 | 0.039 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".