Pan-Canadian Oncology Drug Review (pCODR): A unique model to support harmonization of cancer drug funding decisions in Canada.
Bibliographic record
Abstract
41 Background: Unlike most other countries, Canada has a dedicated HTA process to review cancer drugs. pCODR, a program of CADTH, conducts thorough and objective evaluations of clinical and economic evidence; as well as considering clinician and patient perspectives, and using this information to make recommendations to the participating jurisdictions to guide their drug funding decisions. Previously, Canadian provinces had separate regional drug review processes to inform their local funding decisions. The pCODR process reduces duplication of effort by individual funders and ensures that reviews are done in a timely and consistent manner. Methods: In a retrospective review, we identified all anticancer drugs reviewed by pCODR from July 2011 to March 2018. Results: As of March 31, 2018, pCODR has issued 103 notifications to implement a final recommendation. Of note, 96% of the submissions include patient group input and 88% of the submissions include clinician input. The median time to complete a review is 146 business days. The pCODR Expert Review Committee has issued 21 negative recommendations, while the remainder were either positive recommendations (n = 10), or conditional recommendations (n = 72). The “condition” that must be addressed most frequently in the conditional recommendations is the cost-effectiveness of the drug. Over 75% of the 82 positive and conditional recommendations have received uptake from one or more participating jurisdictions. The concordance rates are as follows: Conclusions: With the implementation of the pCODR process, there is greater harmonization in cancer drug funding decisions and supports equitable access across Canada.[Table: see text]
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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 | 0.178 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".