Deliberative Processes by Health Technology Assessment Agencies: A Reflection on Legitimacy, Values and Patient and Public Involvement Comment on "Use of Evidence-informed Deliberative Processes by Health Technology Assessment Agencies Around the Globe"
Bibliographic record
Abstract
Legitimacy of deliberation processes leading to recommendations for public financing or clinical practice depends on the data considered, stakeholders involved and the process by which both of these are selected and organised. Oortwijn et al provides an interesting exploration of processes currently in place in health technology assessment (HTA) agencies. However, agencies are struggling with core issues central to their legitimacy that goes beyond the procedural exploration of Oortwijn et al, such as: how processes reflect the mission and values of the agencies? How they ensure that recommendations are fair and reasonable? Which role should be given to public and patient involvement? Do agencies have a positive impact on the healthcare system and the populations served? What are the drivers of their evolution? We concur with Culyer commentary on the need of learning from doing what works best and that a reflection is indeed needed to "enhance the fairness and legitimacy of HTA."
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 | 0.049 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.020 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.085 | 0.094 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".