Is it time to revisit orphan drug policies?
Bibliographic record
Abstract
Yes, for equity’s sake The number of new treatments for rare disorders—so called orphan drugs—has increased over the past decade. This is a testament to the success of the Orphan Drug Act in the United States and the Orphan Drugs Regulation in Europe.1 2 The large number of treatments in late stage development indicates that this success is likely to be sustained.3 However, this poses a substantial challenge for healthcare systems because the prices charged for these drugs make it impossible for them to meet conventional measures of good value.4 Increasingly, access to orphan drugs is likely to be restricted, causing political problems for governments and reducing the return to manufacturers from their research investment. To date, many healthcare payers have exempted orphan drugs from formal value assessment, arguing that society values equal opportunity for people with rare and common conditions enough to justify the high costs. Until now this has been assumed, rather than being based on robust evidence.5 In the linked survey (doi:10.1136/bmj.c4715), Desser and colleagues asked a representative sample of the Norwegian general population whether society should pay more to treat rare diseases than it does …
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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 | 0.016 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.098 | 0.072 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".