Individualized treatment with sunitinib versus standard dosing with sunitinib or pazopanib in patients with metastatic renal cell carcinoma (mRCC): Results from the Canadian Kidney Cancer information system (CKCis).
Bibliographic record
Abstract
e16078 Background: Recent analysis using CKCis showed that mRCC patients receiving first-line sunitinib (S) had better survival than patients receiving pazopanib (P) and greater than expected survival for a real world sampling. We conducted further analyses to see if an individualized approach (treatment starting at standard dose/schedule with subsequent schedule/dose alterations based on toxicity) using S results in better outcomes in mRCC patients. Methods: Patients within CKCis diagnosed with clear cell mRCC treated with first-line S or P between January 2011 through December 2015 were analyzed by three treatment groups: 1) S as per individualized approach (SI) 2) S as per product monograph (SS) 3) P as per product monograph (PS). Overall survival (OS) and time-to-treatment failure (TTF) were calculated. Cox regression analysis allowed for adjustment of International Metastatic RCC Database Consortium (IMDC) criteria with age as a continuous variable. Results: A total of 598 patients were identified, 351 patients in SI, 151 patients in SS, and 92 patients in PS. Baseline characteristics are noted in Table 1. Median OS was improved in SI vs SS (37.9 vs 22.3 months (m), p<0.001) and SI vs PS (37.9 vs 19.6 m, p<0.001). TTF was better in SI vs SS (12.9 vs 5.6 m, p<0.001) and SI vs PS (12.9 vs 7.0 m, p<0.001). SS vs PS showed no difference in median OS (22.3 vs 19.6 m, p=0.51) or TTF (5.6 vs 7.0 m, p=0.24). Adjusted hazard ratios were: SS vs SI (OS 1.41, p=0.056; TTF 1.77, p<0.001) and PS vs SI (OS 2.18, p<0.001; TTF 1.43, p=0.040). Conclusions: Improvement in OS and TTF is seen using an individualized approach to mRCC patients supporting the growing body of evidence endorsing this practice. Further prospective validation awaits the NCT01499121 study. [Table: see text]
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".