Les systèmes d’évaluations et procédures de prix et remboursement des médicaments au Royaume-Uni et en France, une comparaison et des évolutions prévisibles
Bibliographic record
Abstract
The French and British health technology appraisals and pricing systems are based on opposite flows and concepts. In one hand, the French system starts with a clinical assessment and the development of the "avis" enclosing the SMR and ASMR (i.e. an expert guided subjective clinical assessment), followed by pricing negotiations. The pricing negotiations are parameterised by the ASMR level and the subsequent production of a "convention" by the CEPS that also regulates profits by Medicine. In the other hand, the UK, a free-pricing system prior to the proceedings of full health technology appraisals. HTAs deem the opportunity cost with the cost per QALY, and NICE Guidance is associated to statutory funding within the NHS. Companies’ profits are administered through the PPRS with either the voluntary scheme or the statutory scheme. Regulating company profits rather than a product profits might be more beneficial to investment and might generates savings to a country. The forthcoming European JCA warrants collaboration and perhaps less subjective clinical approaches within the Union, whilst, the UK (outside the EU) relies on international cooperation with a wider application of the cost per QALY in Canada, Australia and the USA.
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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 | 0.069 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".