Status of oncology drugs with a conditional approval: A cross‐sectional comparison of the Food and Drug Administration and Health Canada
Bibliographic record
Abstract
AIMS: This study looks at the status of the same drugs conditionally approved by the Food and Drug Administration and Health Canada for the same oncology indication. METHODS: Lists of oncology drugs with a conditional approval from the Food and Drug Administration and Health Canada were generated and drug pairs with the same indication were matched. Drugs were categorized by status (benefits verified, benefits not yet verified and withdrawn); the median length of time between approval and status was calculated and the 2 regulators were compared. RESULTS: Eighty-nine drug pairs were analysed. Forty-five (50.6%) drugs had benefits verified by both regulators, 16 (18.0%) had benefits that were not yet verified by both regulators and 6 (6.7%) had been withdrawn by both regulators. The 45 drugs with verified benefits were on the US market for a median of 1120 days before verification compared to a median of 1345 days on the Canadian market (P = .1063, Mann-Whitney test). The 16 drugs with benefits not yet verified had been on the market in the USA for a median of 1373 days vs. a median of 1025 days in Canada (P = .3414, Mann-Whitney test). Out of 67 drugs with concordant status, 14 (20.9%) were on the US market for >1800 days and 27 (40.3%) were on the Canadian market for that length of time (P = .0127, z test). CONCLUSIONS: Reforms to conditional approval systems in both countries need to be made to reduce the time products are on the market without a final status.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".