Characterization of the benefit-risk profile of nivolumab + ipilimumab (N+I) <i>v</i> sunitinib (S) for treatment-naïve advanced renal cell carcinoma (aRCC; CheckMate 214).
Bibliographic record
Abstract
686 Background: The phase 3 CheckMate 214 study demonstrated superior efficacy for N+I v S in intermediate/poor-risk aRCC patients (pts), with manageable safety (Escudier, ESMO 2017). Here we report additional data to define benefit-risk. Methods: Pts with clear-cell aRCC were randomized 1:1 to N 3 mg/kg + I 1 mg/kg every 3 wk for 4 doses followed by N 3 mg/kg every 2 wk, or S 50 mg daily orally for 4 wk (6-wk cycles). Primary endpoints were efficacy parameters in intermediate/poor-risk pts. Secondary endpoints included adverse event (AE) incidence in all treated pts. Select AEs were defined as AEs pooled by organ category that may differ from AEs caused by non-immunotherapies, may require immunosuppression, and whose early recognition may mitigate severe toxicity. Results: 1096 pts were randomized (pts treated: N+I: n = 547; S: n = 535). N+I showed statistically significant OS benefit, significantly higher ORR, numerically longer PFS, and better symptom control v S. 79% of N+I pts received all 4 I doses. Drug-related grade 3-5 AEs occurred in 46% with N+I v 63% with S; drug-related AEs leading to discontinuation occurred in 22% v 12%. Drug-related select AEs resolved in 72%-92% of N+I pts, except endocrinopathies (43%; Table). An analysis of the relationship between safety and efficacy will be presented. Conclusions: Drug-related select AEs with N+I were manageable, with the vast majority resolving except endocrinopathies. These additional data further support the favorable benefit-risk profile of N+I v S in CheckMate 214. Clinical trial information: NCT02231749. [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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".