Post‐market safety warnings for drugs approved in <scp>C</scp>anada under the <scp>N</scp>otice of <scp>C</scp>ompliance with conditions policy
Bibliographic record
Abstract
AIMS: Health Canada has developed a pathway to approve drugs that have limited efficacy and safety data, the Notice of Compliance with conditions (NOC/c) policy. Increased safety reporting is required for these drugs but there has not been any systematic review of their post-market safety. This study compares safety warnings for NOC/c drugs with drugs with a priority and a standard review. METHODS: A list of drugs approved between January 1 1998 and March 31 2013 was developed and serious safety warnings for these drugs were identified. Drugs were put into one of three groups based on the way that they were approved. Kaplan-Meier curves were generated to examine the likelihood of NOC/c drugs receiving a serious safety warning compared with drugs with a priority and a standard review. The time spent in the review process for each of the groups was also measured. RESULTS: Compared with drugs with a priority review, NOC/c drugs were not more likely to receive a serious safety warning (P = 0.5940) but were more likely than drugs with a standard review (P = 0.0113). NOC/c drugs spent less time in the review process compared with drugs with a standard review. CONCLUSIONS: Possible reasons for the increase likelihood of a serious safety warning are the limited knowledge of the safety of NOC/c drugs when they are approved and the length of time that they spend in the review process. Health Canada should consider spending longer reviewing these drugs and monitor their post-market safety more closely.
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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 | 0.099 | 0.344 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".