Expanding the scientific basis of health technology assessment: A research agenda for the next decade
Bibliographic record
Abstract
OBJECTIVES: The complexity of health technology assessment (HTA) has increased, in part because of its evolution through three distinct phases: the machine, the clinical outcomes, and the delivery models. However, the theoretical foundation for the field remains underdeveloped. METHODS: It is high time for HTA to bring together aspects of conceptual and theoretical works from other fields to strengthen the foundation of HTA. RESULTS: Many challenges await the further development of HTA. They can be captured around three research themes: adapting HTA to an evolving analysis object; translating HTA results into policy, management, and practice decisions; and evaluating organizational models of HTA. CONCLUSIONS: Consolidating the scientific basis of HTA is essential if we are to succeed in increasing the relevance of HTA in some of the most challenging health-related decisions that we will make as individuals and societies.
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 | 0.104 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.017 | 0.041 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.025 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 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".