European governments should align medicines pricing practices with global transparency norms and legal principles
Bibliographic record
Abstract
Advocates of transparency argue that openness about a medicine's price components (ex. research and development (R&D) costs, production costs, discounts and rebates etc.) is essential to know whether the price is ‘fair’ to the seller and the buyer.4 This Comment highlights the normative basis for transparency, and recent initiatives in Europe supporting increased transparency of medicines and medical product price components. Aligning governments’ transparency practices with their commitments and legal principles is urgent as cross-country collaborations (for medicines information sharing, and joint assessment and price negotiation) take shape in Europe, and as the European Commission expands its role in centralized medicines procurement.5
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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 | 0.078 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.025 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".