Bibliographic record
Abstract
The number of drugs for orphan indications has been increasing significantly in Canada and the federal government recently announced an investment of $1.5 billion dollars over 3 years primarily directed at helping to fund the cost of these drugs. There are claims and counterclaims about what percent of Food and Drug Administration (FDA) orphan drugs are available in Canada and how delayed these drugs are in being approved by Health Canada. This study uses FDA and Health Canada databases and data from three health technology assessment agencies and one drug bulletin to provide objective data about the percent of FDA approved drugs that were also approved by Health Canada, any delays in Canadian approval and the additional therapeutic value of new orphan drugs. Decisions about what drugs should be publicly covered and how long it took to make those decisions were not investigated. From 1999 to 2022, the FDA approved 326 new drugs for an orphan indication and Health Canada approved 231 (70.9%) for the same indication. The median time between FDA and Health Canada approval was 346 days (interquartile range [IQR] 181, 785). The percent rated as major improvements declined from 50% of the total in 2004-2008 to 13.6% in 2019-2022. These findings need to be taken into account as Canada develops an orphan drug policy and decides on criteria for funding this group of drugs. Specifically, when high quality evidence about the additional therapeutic value of orphan drugs is not available at the time of approval, risk sharing funding agreements with manufacturers should be put in place. Manufacturers should understand that if the results of post-market trials do not provide convincing evidence of value, funding will be withdrawn. Finally, the quality of any research plan should be used to prioritize candidates for federal funding.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".