Is there another reason for the change in the rate of recommendations to reimburse from Canada’s drug agency?
Bibliographic record
Abstract
Abstract Objective To assess whether the development of the pan-Canadian Pharmaceutical Alliance (pCPA) could be a reason for the change in the rate of recommendations to reimburse from Canada’s Drug Agency (CDA). Methods Data on submissions and recommendations were obtained from CDA’s reimbursement review reports. Only the first submission for each drug-indication combination between 2009 and 2023 was included. Recommendations to reimburse with or without clinical criteria and/or conditions were combined as positive recommendations, while do not reimburse recommendations were categorized as negative. Rates of positive and negative recommendations for submissions made in 2009 to 2013 (before and during the pCPA’s initial development), 2014 to 2018 (when the pCPA was formalized but not a standalone agency), and 2019 to 2023 (when pCPA transitioned into a standalone organization) were calculated for all drugs, oncology drugs, and non-oncology drugs. Key findings There has been a significant increase in the rate of positive recommendations for non-oncology medicines between 2009 and 2013 when the pCPA was not fully active and the rate in the following 10 years (P < .0001). Oncology drugs had a higher rate of positive recommendations than non-oncology drugs prior to the pCPA becoming fully active and the rate stayed approximately the same. Conclusion The creation of the pCPA has allowed CDA to step back from its role as price gatekeeper and pass it to the pCPA, permitting CDA to focus on its health technology assessment function. The pCPA’s establishment is an alternative reason for the significant increase in positive CDA recommendations.
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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 | 0.044 | 0.178 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| 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".