Real world outcomes of nivolumab and cabozantinib in metastatic renal cell carcinoma: Results from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC).
Bibliographic record
Abstract
615 Background: The immuno-oncology (IO) checkpoint inhibitor nivolumab and the tyrosine kinase inhibitor (TKI) cabozantinib have both been shown in phase III clinical trials to be effective in metastatic renal cell carcinoma (mRCC) after progression on first-line therapy. We sought to explore the real-world efficacy of these therapies in second-line mRCC. Methods: Using the IMDC database, a retrospective analysis was performed on mRCC patients treated with second-line nivolumab or cabozantinib. Baseline characteristics and IMDC risk factors were collected. Overall survival (OS), time to treatment failure (TTF), and response rates were determined for each therapy. Multivariable Cox regression analysis was performed to determine survival differences. Results: 225 patients were treated with nivolumab and 53 with cabozantinib. There was no significant difference in OS identified, with a mOS for nivolumab of 22.1 months (95% CI 17.18 – NR) and 23.7 months (95% CI 15.52 vs. NR) for cabozantinib, p = 0.6053. The TTF was also similar, with 6.90 months (95% CI 4.60 – 9.20) for nivolumab versus 7.39 months (95% CI 5.52 – 12.85) for cabozantinib, p = 0.1983. The adjusted hazard ratio (HR) for nivolumab vs. cabozantinib was 1.297 (95% CI – 0.728 – 2.312), p = 0.3775. Conclusions: Nivolumab and cabozantinib appear to have similar efficacy in terms of OS and TTF in this real-world patient population, thus both novel agents are reasonable therapeutic options for patients progressing after initial first-line therapy. [Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".