Time to potential for listing of new drugs on public and private formularies in Canada: a cross-sectional study
Bibliographic record
Abstract
<h3>Background:</h3> 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). <h3>Methods:</h3> 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. <h3>Results:</h3> 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 (<i>p</i> = 0.2). Additional therapeutic value did not make a difference in the delay (<i>p</i> = 0.3) and companies did not take full advantage of the opportunity to file early submissions with CADTH. <h3>Interpretation:</h3> 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 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) | 0.001 | 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".