Time to potential for listing of new drugs on public and private formularies in Canada: a cross-sectional study
Bibliographic record
Abstract
Background: Information about the timing involved in various stages of making new drugs available to Canadians is important for understanding how a national pharmacare plan will affect timely access to new drugs. I explored the timing of the various steps between receiving a Notice of Compliance and a decision by the pan-Canadian Pharmaceutical Alliance (pCPA). Methods: I gathered data from various databases (Canadian and other) about new drugs approved between 2011 and 2020, including generic names, date of application for approval (New Drug Submission [NDS]), date of Notice of Compliance, date of marketing, dates when a submission was made to the Canadian Agency for Drugs and Technologies in Health (CADTH) and the pCPA, and when these agencies made a decision. Results: Marketing dates were available for 301 of the 337 new drugs approved. The median time from NDS to marketing was less than the time to a positive pCPA decision for all years between 2011 and 2020. There was no significant change in the difference between the 2 periods over time (p = 0.2). Additional therapeutic value did not make a difference in the delay (p = 0.3) and companies did not take full advantage of the opportunity to file early submissions with CADTH. Interpretation: The delay between when drugs could be listed on private compared with public formularies was at least 1 year. If a national pharmacare plan is instituted, one of the priorities should be to concentrate on consolidating and working to shorten the CADTH and pCPA processes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 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) | 0.006 | 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".