Expensive Drug Prices for Rare Cancers: Are Patients Truly Benefitting?
Bibliographic record
Abstract
Cancer medicines comprise the largest proportion of expensive drugs for rare diseases (EDRDs). The US Orphan Drug Act (ODA) (Office of Inspector General, Department of Health and Human Services 2001) encourages pharmaceutical manufacturers to develop medicines for rare diseases through a range of financial incentives, which has shifted the development of cancer medicines to rare cancer subtypes. Although certain medicines approved through the ODA have revolutionized cancer treatment, only half demonstrate added therapeutic benefit compared to existing alternatives. Canadian regulators should ensure that cancer medicines that receive fast-track approval through the Health Canada Notice of Compliance with conditions offer benefit to Canadian patients. Furthermore, payers might engage in methods for reassessment and renegotiations over the medicines' lifespan.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.057 | 0.028 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".