Trends of Utilization of Systemic Therapies for Metastatic Renal Cell Carcinoma in the Canadian Health Care System
Bibliographic record
Abstract
PURPOSE: Standard-of-care therapies for metastatic renal cell carcinoma (mRCC) have greatly evolved. However, the availability of emerging options in global health care systems can vary. We sought to describe the integration and usage of systemic therapies for mRCC in Canada since 2011. METHODS: We included patients with mRCC enrolled in the Canadian Kidney Cancer Information System, a prospective cohort of patients from 14 Canadian academic centers, who received systemic therapy from January 1, 2011, to December 31, 2021. Patients were stratified by treatment era (cohort 1: 2011-2015, cohort 2: 2016-2021). Stacked bar charts were used to present treatment proportions; Sankey diagrams were used to show the evolution of treatment sequencing between the two cohorts. RESULTS: Four thousand one hundred seven patients were diagnosed with mRCC, of whom 2,752 (67%) received systemic therapy. Among these patients, mean age was 64 years, 74% were male, 75% had clear cell histology, and International Metastatic RCC Database Consortium risk classification was favorable, intermediate, and poor in 16%, 56%, and 28%, respectively. Utilization of immune checkpoint inhibition (ICI)-based treatments has increased in Canada and reflects global and local patterns of approval and adoption. The use of therapies after doublet ICI has mostly shifted toward vascular endothelial growth factor-tyrosine kinase inhibitors (VEGF-TKIs) that were previously used in first line with subsequent treatments reflecting approved and available agents after previous VEGF-TKI. Clinical trial participation among patients who received systemic therapy was 18% in first, 21% in second, and 24% in third line. CONCLUSION: In Canada's publicly funded health care system, availability of standard mRCC therapies broadly reflects access from government-funded clinical trials and compassionate access program sources. In an evolving therapeutic landscape, ongoing advocacy is required to continue to facilitate patient access to efficacious therapies.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".