Lessons from Canada’s notice of compliance with conditions policy for the life-cycle regulation of drugs
Bibliographic record
Abstract
Innovative health technologies are not well regulated under current pathways, leading regulators to adopt contextual, life-cycle regulatory models, which authorize drugs based on earlier clinical evidence subject to the conduct of post-market trials that confirm clinical benefit and safety. In this paper, we evaluate all drugs authorized in Canada under the Notice of Compliance with conditions (NOC/c) policy from 1998 to 2021 to analyze its function, identify challenges and areas for improvement, and make recommendations to inform Health Canada's regulatory reforms. We analyzed a sample of 148 drugs authorized between 1998 and 2021, including characteristics about the pre- and post-market clinical trials, finding that most NOC/c authorizations are based on one, single-arm clinical trial using a surrogate endpoint. Post-market trials are more likely to be randomized, Phase III trials but mostly use surrogate endpoints. Based on our findings, we recommend increasing decision-making transparency throughout the regulatory process, developing comprehensive eligibility criteria for selecting appropriate health technologies, modernizing pre-market evidence requirements, adopting a more active role in designing post-market trials, and utilizing automatic expiry, stronger penalties, and ongoing disclosure of the status of post-market trials to promote compliance.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".