Niche Markets and Evidence Assessment in Transition: A Critical Review of Proposed Drug Reforms
Bibliographic record
Abstract
In response to rising demands and treatment costs, and the need to achieve better value for money in the face of tight fiscal constraints, both the National Health Service and the public drug reimbursement system are undergoing important reforms. Concurrently, the pharmaceutical sector itself is also alleged to be experiencing significant changes, perhaps most notably, a decline of the blockbuster model of drug development and a growing focus on niche market products. As pharmaceutical development strategies evolve and the resulting drug products become more complex, regulatory and policy responses must be able to evolve along with them. We explore how in numerous jurisdictions, including the UK, proposals for 'adaptive licensing' on the regulatory side and 'performance-based risk sharing agreements' on the funding side are shifting the focus of drug regulation and reimbursement towards more incremental access to new therapies and more post-market evidence generation. However, serious questions remain about how such reforms can be successfully implemented and whether they can balance demands for earlier access to promising new therapies with the need for robust evidence on safety, efficacy, and cost-effectiveness.
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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 | 0.046 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".