OP134 Pan-Canadian Oncology Drug Review Decisions And Access To Anticancer Treatments In Canada
Bibliographic record
Abstract
Introduction: The Canadian Agency for Drugs and Technologies in Health (CADTH) pan-Canadian Oncology Drug Review (pCODR) plays an important role in public reimbursement decision-making for oncology drugs in Canada. This research studies the relation of positive pCODR decisions to new cancer treatment and their subsequent inclusion in Canada's public drug plans. Methods: We studied all oncology drugs that received an approval from Health Canada and were reviewed by the pCODR from inception till 26th Sep, 2017. The data was obtained from CADTH and Health Canada. Data such as indication, submission type and date, recommendation date, final recommendation, and subsequent provincial funding status was extracted and analyzed. Impact was evaluated by analyzing the percentage of drug submissions with assessment outcome (positive recommendation rate and conditional recommendation rate) and time taken for the final decision (recommendation gap). The percentage of drugs included in public formulary after positive recommendation by pCODR (coverage rate) and the gap in days from positive recommendation to subsequent coverage in provinces (coverage gap) was also assessed. Results: Among 119 drugs reviewed by pCODR, the positive recommendation rate was eight percent. Nine applications comprising seven drugs for six indications received positive recommendations, and genitourinary treatments received maximum positive recommendations. The conditional recommendation rate was 52 percent; 62 applications of 45 drugs for 46 indications received conditional recommendation. Lymphoma and myeloma treatments received maximum conditional recommendations. The average recommendation gap for positive and conditional recommendations was 180 and 172 days, respectively. The coverage rate for drugs with positive recommendation was 100 percent for all provinces except 89 percent for Newfoundland and Labrador, and 67 percent for Prince Edward Island. Among the provinces, British Columbia had a maximum of 433 days and Saskatchewan has the minimum of 165 days coverage gap. Conclusions: Despite Health Canada's approval, only a fraction of oncology drugs receive positive pCODR recommendation; furthermore, provincial drug plans take time to include these in the reimbursement formularies. While health technology assessment is crucial for appropriate allocation of limited resources, efforts should also be made to reduce access barriers, particularly to positively recommended oncology drugs inclusion in provincial formularies.
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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 | 0.008 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".