Health Canada’s Use of its Notice of Compliance With Conditions Drug Approval Policy: A Retrospective Cohort Analysis
Bibliographic record
Abstract
Health Canada has developed its Notice of Compliance with conditions (NOC/c) policy to get promising new drugs for serious diseases to market faster than would be possible through its standard approval process. Companies can receive an NOC/c for a new drug or a new indication based on incomplete evidence in return for agreeing to conduct post-market studies. This paper investigates the additional therapeutic gain from drugs approved under this policy, the percent of drugs that have fulfilled their conditions, and the length of time for fulfillment. From the inception of the policy in 1998 to the end of 2017, 89 new drugs and new indications for existing drugs received an NOC/c. Therapeutic evaluations were available for 78 of the drugs, and 54 offered only minimal or no gains over existing products. Fifty NOC/c were fulfilled, 31 were not fulfilled, and 8 were withdrawn. The median time to fulfillment was 1,040 days. Twelve NOC/c took more than 5 years to fulfill their conditions. The unfulfilled NOC/c had been issued for a median of 1,161 days, and 10 had been issued more than 5 years. The value of the NOC/c policy to patients is uncertain.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.009 |
| 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) | 0.004 | 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".