Real-World Efficacy and Safety of Avelumab Plus Axitinib in Metastatic Renal Cell Carcinoma: Results from the Ambispective RAVE-Renal Study
Bibliographic record
Abstract
BACKGROUND: The RAVE-Renal study was conducted to evaluate the real-world efficacy and safety of avelumab plus axitinib as a first-line therapy for patients with metastatic renal cell carcinoma (mRCC). METHODS: RAVE-Renal was a multicenter, noninterventional, ambispective study with both retrospective and prospective components. The study included adult patients with histologically confirmed mRCC, measurable disease per RECIST version 1.1, and no prior systemic therapy. Patients received avelumab (800 mg intravenously every 2 weeks) plus axitinib (5 mg orally twice daily). The primary endpoints were median progression-free survival (PFS) and objective response rate (ORR). The secondary endpoints included median OS, 1-year overall survival (OS) rate, and safety. RESULTS: A total of 125 patients from 13 sites were enrolled, with a median follow-up of 16.1 months. The median age was 61.0 years. The study population comprised 35.3% favorable, 49% intermediate, and 15.7% poor IMDC risk patients. The median PFS was 14.9 months (95% CI, 11.72-19.08). The ORR was 44.3% (95% CI, 32.5-56.1). The clinical benefit rate was 93.4%. The 1-year OS rate was 71.2%, with the median OS not reached. Any-grade treatment-related adverse events (TRAEs) occurred in 99 (79.2%) cases, including grade ≥3 TRAEs in 24 (19.2%). CONCLUSIONS: Avelumab in combination with axitinib showed clinical benefits in a real-world setting, consistent with findings from a pivotal trial. The regimen was effective and well tolerated across various patient subgroups.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".