First-line sunitinib or pazopanib in metastatic renal cell carcinoma: The Canadian experience
Bibliographic record
Abstract
Introduction: Clinical trial data has shown pazopanib to be noninferior in overall survival (OS) compared to sunitinib as first-line treatment for metastatic renal cell carcinoma (mRCC). The purpose of this study was to evaluate outcomes and compare dose-modifying toxicities of mRCC patients treated with suntinib or pazopanib in the real-world setting.Methods: Data were collected on mRCC patients using the prospective Canadian Kidney Cancer Information System (CKCis) database from January 2011 to November 2015. Statistical analyses were performed using Cox regression adjusted for several risk factors and the Kaplan-Meier method.Results: We identified 670 patients treated with sunitinib (n=577) and pazopanib (n=93). There were no significant differences in International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) risk groups (p=0.807). Patients treated with sunitinib had improved OS compared with pazopanib (median 31.7 vs. 20.6 months, p=0.028; adjusted hazard ratio [aHR] 0.60; 95% confidence interval [CI] 0.38‒0.94). Time to treatment failure (TTF) was numerically, but not statistically, improved with sunitinib (medians 11.0 vs. 8.4 months, p=0.130; aHR 0.87; 95% CI 0.59‒1.28). Outcomes with individualized dosing on sunitinib were unavailable for this analysis. Patients treated with sunitinib had a higher incidence of mucositis, hand-foot syndrome, and gastroesophageal reflux disease; patients treated with pazopanib had a higher incidence of hepatotoxicity.Conclusions: In Canadian patients with mRCC, treatment with sunitinib appears to be associated with an improved OS compared to pazopanib in the first-line setting. Patient selection factors and the contemporary practice of individualized dosing with sunitinib may contribute to these real-world outcomes and warrant further investigation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".