The ‘Top 10’ Challenges for Health Technology Assessment: INAHTA Viewpoint
Bibliographic record
Abstract
The International Network of Agencies for Health Technology Assessment (INAHTA) spans the globe as a network of 50 publicly-funded health technology assessment (HTA) agencies supporting health system decision making for 1.4 billion people in thirty countries. Agency members are non-profit HTA organizations that are part of, or directly support, regional or national governments. Recently, INAHTA surveyed its members to gather perspectives from agency leadership on the most important issues in HTA today. This paper describes the top 10 challenges identified by INAHTA members. Addressing these challenges requires a call for action from INAHTA member agencies and the many other actors involved in the HTA ecosystem. In opening this call for action, INAHTA will lead the way; however, a comprehensive undertaking from all players is needed to effectively address these challenges and to continue to evolve HTA in its role as a strong and effective contributor to health systems.
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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 | 0.109 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.038 | 0.035 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.020 | 0.036 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".