Making Sure Orphan Drugs Don’t Get Left Behind
Bibliographic record
Abstract
Orphan drugs developed to treat rare diseases are expensive, thus making it difficult for provincial governments to cover their costs and for patients to acquire them. However, a streamlined method of setting guidelines for coverage using a cost-based regulatory model could help patients get access to the drugs while ensuring manufacturers are fairly compensated. Currently, governments can justify covering cost-effective drugs. Manufacturing costs, including research and development, typically put orphan drugs over any threshold of cost-effectiveness because so few patients use them. Thus, governments either decline coverage or end up funding the drugs under pressure from patient advocacy groups. Without adequate compensation for their efforts, manufacturers will have no incentive to develop orphan drugs. A cost-based regulatory model, including yardstick pricing, would improve access to orphan drugs because it creates incentives for companies to lower their costs. Yardsticking means that prices are set using industry benchmarks and firms that successfully lower their costs below those of competitors can profit by it. Under this system, the government could still apply an initial cost-effectiveness test. In cases where that threshold is not met, the cost-based regulatory model would be used to decide upon the maximum price at which the drug would be covered. This would be done through an estimated, benchmarked, capital cost based on the average cost of drug development across the pharmaceutical industry, and take into consideration the probability of success. Such an approach would allow governments to bargain over a drug’s price, yet still create incentives for companies to develop orphan drugs at the lowest possible costs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.040 | 0.023 |
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".