Real-World Outcomes of Nivolumab and Cabozantinib in Metastatic Renal Cell Carcinoma: Results from the International Metastatic Renal Cell Carcinoma Database Consortium
Bibliographic record
Abstract
Objectives: In the present study, we explored the real-world efficacy of the immuno-oncology checkpoint inhibitor nivolumab and the tyrosine kinase inhibitor cabozantinib in the second-line setting. Methods: Using the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) dataset, a retrospective analysis of patients with metastatic renal cell carcinoma (MRCC) treated with nivolumab or cabozantinib in the second line after prior therapy targeted to the vascular endothelial growth factor receptor (VEGFR) was performed. Baseline characteristics and imdc risk factors were collected. Overall survival (OS) and time to treatment failure (TTF) were calculated using Kaplan–Meier curves. Overall response rates (ORRS) were determined for each therapy. Multivariable Cox regression analysis was performed to determine survival differences between cabozantinib and nivolumab treatment. Results: The analysis included 225 patients treated with nivolumab and 53 treated with cabozantinib. No significant difference in median OS was observed: 22.10 months [95% confidence interval (CI): 17.18 months to not reached] with nivolumab and 23.70 months (95% CI: 15.52 months to not reached) with cabozantinib (p = 0.61). The TTF was also similar at 6.90 months (95% CI: 4.60 months to 9.20 months) with nivolumab and 7.39 months (95% CI: 5.52 months to 12.85 months) with cabozantinib (p = 0.20). The adjusted hazard ratio (HR) for nivolumab compared with cabozantinib was 1.30 (95% CI: 0.73 to 2.3), p = 0.38. When adjusted by IMDC criteria and age, the HR was 1.32 (95% CI: 0.74 to 2.38), p = 0.35. Conclusions: Real-world IMDC data indicate comparable OS and TTF for nivolumab and cabozantinib. Both agents are reasonable therapeutic options for patients progressing after initial first-line VEGFR-targeted therapy.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".