Kidney Cancer Research Network of Canada (KCRNC) consensus statement on the role of adjuvant therapy after nephrectomy for high-risk, non-metastatic renal cell carcinoma: A comprehensive analysis of the literature and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
INTRODUCTION: The Kidney Cancer Research Network of Canada (KCRNC) collaborated to prepare this consensus statement about the use of target agents as adjuvant therapy in patients with non-metastatic renal cell carcinoma (nmRCC) after nephrectomy. We reviewed the published data and performed a meta-analysis of studies that focused on vascular endothelial growth factor receptor (VEGFR) tyrosine kinase inhibitors (TKIs). METHODS: A systematic literature search identified seven trials on adjuvant target therapy in nmRCC. Three trials, the ASSURE, S-TRAC, and PROTECT, focused on VEGFR TKIs and represented the focus of the study, including a meta-analysis combining their data on disease-free survival (DFS) and overall survival (OS). RESULTS: The ASSURE trial showed no DFS or OS benefit of TKIs over placebo after one year of adjuvant sorafenib or sunitinib. In contrast, the S-TRAC trial showed improved DFS after one year of adjuvant sunitinib using central review process, but not using investigator review process. No OS benefit was recorded in either study. Recently, the PROTECT trial also showed no DFS or OS benefit when one year of adjuvant pazopanib was compared to placebo. Meta-analyses of the pooled DFS and OS estimates from all three trials resulted in DFS and OS hazard ratios of 0.87 (95% confidence interval [CI] 0.73-1.04) and 1.04 (95% CI 0.89-1.22), respectively. CONCLUSIONS: Data from three available clinical trials of adjuvant VEGFR TKIs vs. placebo do not currently support the use of adjuvant TKI therapy as standard of care after nephrectomy for nmRCC. At this time, adjuvant TKI-based adjuvant therapy is not recommended for routine use after nephrectomy for high-risk nmRCC, but highly motivated patients may benefit from a discussion with their oncologist regarding the risks and benefits of adjuvant TKI.
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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 | 0.201 | 0.220 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".