Harmonization of evidence requirements for health technology assessment in reimbursement decision making
Bibliographic record
Abstract
As more countries use HTA to inform decisions on the reimbursement of health technologies, harmonization of evidence requirements between jurisdictions has been proposed, mainly on the grounds of improved efficiency. Harmonization has the potential to avoid duplication of effort for both manufacturers and HTA bodies involved in preparing and reviewing HTA submissions for innovative technologies. However, it also carries risks of loss of local control over decisions, the application of general data standards which are not universally accepted and slowing the rate of development of innovation in the analytical disciplines supporting HTA. This study reviews the issues associated with harmonization taking into account the perspectives of the multiple stakeholders. This study draws on experiences from recent initiatives intended to promote the harmonization of HTA and experience from related fields, particularly regulatory approval of new medical technologies.
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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 | 0.748 | 0.791 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.028 | 0.019 |
| Open science | 0.013 | 0.022 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".