A history of health technology assessment at the European level
Bibliographic record
Abstract
This study summarizes the experience with health technology assessment (HTA) at the European level. Geographically, Europe includes approximately fifty countries with a total of approximately 730 million people. Politically, twenty-seven of these countries (500 million people) have come together in the European Union. The executive branch of the European Union is named the European Commission, which supports several activities, including research, all over Europe and in many other parts of the world. The European Commission has promoted HTA by several policy positions and has funded a series of projects aimed at strengthening HTA in Europe. Around fifteen of the European countries now have formal national programs on HTA and some also have regional public programs. All countries that are members of the European Union and do not have a national approach to HTA have an interest in becoming more involved. The HTA projects sponsored by the European Commission have focused on networking and collaboration among established agencies and institutions for HTA, however, also on capacity building, support, and facilitation in creating mechanisms for HTA in European countries that still do not have any program in the field.
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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 | 0.024 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".