Outcomes based on prior therapy in the phase 3 METEOR trial of cabozantinib versus everolimus in advanced renal cell carcinoma
Bibliographic record
Abstract
BACKGROUND: In the phase 3 METEOR trial, cabozantinib improved progression-free survival (PFS), objective response rate (ORR), and overall survival (OS) versus everolimus in patients with advanced renal cell carcinoma (RCC), after prior antiangiogenic therapy. METHODS: Outcomes were evaluated for subgroups defined by prior therapy with sunitinib or pazopanib as the only prior VEGFR inhibitor, or prior anti-PD-1/PD-L1 therapy. RESULTS: For the prior sunitinib subgroup (N = 267), median PFS for cabozantinib versus everolimus was 9.1 versus 3.7 months (HR 0.43, 95% CI 0.32-0.59), ORR was 16% versus 3%, and median OS was 21.4 versus 16.5 months (HR 0.66, 95% CI 0.47-0.93). For the prior pazopanib subgroup (N = 171), median PFS for cabozantinib versus everolimus was 7.4 versus 5.1 months (HR 0.67, 95% CI 0.45-0.99), ORR was 19% versus 4%, and median OS was 22.0 versus 17.5 months (HR 0.66, 95% CI 0.42-1.04). For prior anti-PD-1/PD-L1 therapy (N = 32), median PFS was not reached for cabozantinib versus 4.1 months for everolimus (HR 0.22, 95% CI 0.07-0.65), ORR was 22% versus 0%, and median OS was not reached versus 16.3 months (HR 0.56, 95% CI 0.21-1.52). CONCLUSIONS: Cabozantinib was associated with improved clinical outcomes versus everolimus in patients with advanced RCC, irrespective of prior therapy, including checkpoint inhibitor therapy.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".