Editorial Promoting economic value in public health
Bibliographic record
Abstract
In the pharmaceutical field, the term ‘fourth hurdle ’ hasbecome common currency in many health systems. It means that only products that can show their cost-effectiveness will gain unrestricted access to patients. The preceding hurdles of safety, efficacy and quality have existed for several decades and have been harmonized within the European Union years ago. In the early 1990s, Australia and Canada pioneered the integration of cost-effectiveness into the regulation of drug reimbursement; by 2005, a survey identified 10 more European countries to consider this aspect in drug regulation.1 Adding France and Germany leads to at least a dozen European countries going this way today. The emphasis of the ‘fourth hurdle ’ is on individual clinical care. ‘Concentrating on drugs and clinical procedures does not really have a great influence on population health’2 Holland criticized after having reviewed the first experience of health
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.024 | 0.033 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".