Disparity in public funding of systemic therapy for metastatic renal cell carcinoma within Canada
Bibliographic record
Abstract
INTRODUCTION: There have been significant advances in systemic therapies for metastatic renal cell carcinoma (mRCC). There are currently 11 drugs approved by Health Canada: sunitinib, sorafenib, pazopanib, axitinib, everolimus, temsirolimus, nivolumab, ipilimumab, cabozantinib, lenvatinib, and pembrolizumab. These novel medications have dramatically altered the prognosis and patient experience. Despite proven benefits and recommendations for funding of most of these drugs, public access has been uneven across Canadian provinces. METHODS: We describe the provincial differences and timelines in public funding for approved systemic therapies for mRCC in Canada. Drug funding data was collected from the pan-Canadian Oncology Drug Review (pCODR) database and provincial drug formularies. Missing information was obtained from provincial cancer center pharmacists or drug formulary managers. We compared these dates to data available through regulatory bodies in the U.S., Europe, and Australia. RESULTS: There have been significant differences in the dates of approval for public funding among the provinces, with lags spanning between two and 57 months. Funding approval was typically earlier in western provinces and those with denser populations, and most delayed in smaller, eastern provinces. Approval timelines in Canada were similar to those in the U.S., Europe, and Australia. CONCLUSIONS: Most drugs approved for use in mRCC are publicly funded for specific patient populations across Canada; however, we illustrate considerable disparities in public funding implementation across the Canadian provinces. These funding lags may create inequities and differences in the patient experience across the Canadian healthcare system.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".