New drug approval times and safety warnings in the United States and Canada, 1992-2011.
Bibliographic record
Abstract
BACKGROUND: New drug approvals in the US and Canada were reviewed in short-term studies in the 1990s. A database of drugs approved in both countries between 1992 and 2011 exists allowing for a longer time horizon to assess trends. OBJECTIVE: To compare review times of drugs approved in the US and Canada over the 20-year period and their duration on the respective markets until any serious safety risk arose. METHODS: Data on submission and approval dates and review type were obtained from the regulatory agencies. RESULTS: 454 drugs were approved in both countries in the 20-year period for which the US median approval time was shorter than the Canadian median by >6 months (382 versus 574 days). Nevertheless, in 2007-11, the median approval times were closer in the two countries (302 and 356 days, respectively). 3% of the drugs were discontinued for safety reasons in both countries. The 10-year survival rate without a serious safety warning was significantly lower in Canada (58.4%) than in the US (69.3%). Being approved in 2002-11 with a shorter review time had the greatest impact on a drug receiving a serious safety warning. CONCLUSIONS: Overall, new drug approval times in the two countries in the last five years were closer, although some important differences remain so that Canadians still wait longer for some new drugs to be approved. The survival rate of a drug without a serious warning decreased substantially in the last decade in both countries, especially in drugs approved with shorter review times.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".