Drug pricing and reimbursement in Europe: Strategy and tactics
Bibliographic record
Abstract
The increase of the research and development (R&D) costs and the time needed for development has resulted in a shortening of the market exclusivity period and has put pressure on fair return on investment for brands. Obtaining a higher price will help contribute to achieve this goal. In the pharmaceutical industry, two subsequent forms of pricing have to be taken into consideration: pricing and reimbursement. The approach where, early on in the R&D process, products are developed that add value to the (existing) treatment options and where enough data are generated during the development will be successful in giving the company a fair return on investment. In Europe (and Canada) where pricing is historically lower and regulated by local agencies, all having different ways to control drug prices, a well-considered R&D and regulatory strategy developed in close collaboration with marketing will lead to success. This success will only materialise with good and fair pricing if, together with the above-mentioned strategy, a well-established communication plan for the new 'consumers' in the healthcare sector (physicians, authorities and reimbursement responsible, as well as patient organisations) is set in place. Thorough communication between the different departments in the company from the first steps in the research through development until final market authorisation will enhance the chances of the product's success in the market.
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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 | 0.027 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.022 | 0.006 |
| 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".