Le processus d’implication des patients dans l’évaluation des technologies de santé à l’HAS
Bibliographic record
Abstract
OBJECTIVE: The French National Authority for Health (HAS) wanted to establish a review about the first months of its new patient involvement process dedicated to drug and medical device evaluation (which began in November 2016). This process relies on written submissions from patient organizations (POs). METHODS: Different sources of information were used: data monitoring, comparison with other contribution processes in HAS, sharing of practices with other Health Technology Assessment (HTA) bodies, feedback discussions with POs, pharmaceuticals firms, and evaluators (internal reviewers and members of Appraisal Committees). RESULTS: There were contributions for 25 drugs among 75 opened to contribution during the first six months. The HAS Board defined three adjustments in September 2017 to improve the procedure: increasing the time for POs to contribute, publishing the contributions on the HAS website, improvement of the presentation of the submission to the committee. Some further necessary reflections were identified such as the information available to POs to elaborate their submission, or the exact nature of intellectual uptake of the contribution during the different stages of assessment/appraisal elaboration. CONCLUSIONS: The different methods proved to be complementary and helped to define adjustments and clarify some future stakes for this new procedure. Data on this kind of process must be routinely collected. Comparisons with other involvement processes and discussions with stakeholders are rather used in dedicated studies or improvements projects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".