IS THE EU<scp>NET</scp>HTA HTA CORE MODEL® FIT FOR PURPOSE? EVALUATION FROM AN INDUSTRY PERSPECTIVE
Bibliographic record
Abstract
OBJECTIVES: The HTA Core Model® was developed to improve the transferability of health technology assessment (HTA) between settings. The model has been used by HTA agencies but is also of interest to manufacturers, for improving internal evidence generation and communicating with other HTA stakeholders. To establish if the model is fit for purpose from an industry perspective, the pharmaceutical company Roche, collaborating with the European Network for HTA (EUnetHTA), conducted an assessment of the model. METHODS: A questionnaire was developed to evaluate all assessment elements in the HTA Core Model v2.0 for their usefulness in meeting payers' evidence needs and demonstrating value. The questionnaire was completed by country affiliate teams working in evidence generation and reimbursement submissions for pharmaceuticals. Survey results were discussed in workshops to ensure consistency and alignment between teams. RESULTS: The questionnaire was completed by six teams. An additional team from global pricing and market access participated in workshops. Model domains pertaining to the health problem and current technology use, technology description, clinical effectiveness, and economic value were considered most important because they meet payers' evidence needs. Overall, the model was considered useful to improve the efficiency of HTA evidence generation, share evidence internally, and communicate value to payers and HTA agencies. CONCLUSIONS: From an industry perspective, the HTA Core Model provides a useful framework and common terminology for efficient generation of transferable HTA evidence. The timeliness, efficiency, and transparency of HTA processes could be improved by a more standardized approach to HTA across settings.
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 | 0.247 | 0.314 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".