Efficacy and safety of lenvatinib plus pembrolizumab vs sunitinib in the East Asian subset of patients with advanced renal cell carcinoma from the <scp>CLEAR</scp> trial
Bibliographic record
Abstract
In the CLEAR trial, lenvatinib plus pembrolizumab met study endpoints of superiority vs sunitinib in the first-line treatment of patients with advanced renal cell carcinoma. We report the efficacy and safety results of the East Asian subset (ie, patients in Japan and the Republic of Korea) from the CLEAR trial. Of 1069 patients randomly assigned to receive either lenvatinib plus pembrolizumab, lenvatinib plus everolimus or sunitinib, 213 (20.0%) were from East Asia. Baseline characteristics of patients in the East Asian subset were generally comparable with those of the global trial population. In the East Asian subset, progression-free survival was considerably longer with lenvatinib plus pembrolizumab vs sunitinib (median 22.1 vs 11.1 months; HR 0.38; 95% CI: 0.23-0.62). The HR for overall survival comparing lenvatinib plus pembrolizumab vs sunitinib was 0.71; 95% CI: 0.30-1.71. The objective response rate was higher with lenvatinib plus pembrolizumab vs sunitinib (65.3% vs 49.2%; odds ratio 2.14; 95% CI: 1.07-4.28). Dose reductions due to treatment-emergent adverse events (TEAEs) commonly associated with tyrosine kinase inhibitors occurred more frequently than in the global population. Hand-foot syndrome was the most frequent any-grade TEAE with lenvatinib plus pembrolizumab (66.7%) and sunitinib (57.8%), a higher incidence compared to the global population (28.7% and 37.4%, respectively). The most common grade 3 to 5 TEAEs were hypertension with lenvatinib plus pembrolizumab (20%) and decreased platelet count with sunitinib (21.9%). Efficacy and safety for patients in the East Asian subset were generally similar to those of the global population, except as noted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".