Everolimus in metastatic renal cell carcinoma (mRCC): Subgroup analysis of patients (pts) with one versus two prior vascular endothelial growth factor receptor tyrosine kinase inhibitor (VEGFR-TKI) therapies enrolled in the phase III RECORD-1 study.
Bibliographic record
Abstract
304 Background: The mammalian target of rapamycin (mTOR) inhibitor everolimus is the only medication to have shown efficacy in a randomized, controlled, phase III clinical trial (RECORD-1) in pts with mRCC after progression on VEGFR-TKIs. Everolimus more than doubled progression-free survival (PFS) vs. placebo (4.9 vs 1.9 months) and reduced the risk of disease progression by 67%. This analysis evaluated the effect of everolimus on survival in pts who had received 1 vs 2 prior VEGFR-TKIs. Methods: Pts with mRCC who progressed on sunitinib (SU) and/or sorafenib (SOR) were randomized (2:1) to receive everolimus 10 mg/day (n = 277) or placebo (n = 139) plus best supportive care in the double- blind, phase III RECORD-1 study (ClinicalTrials.gov: NCT00410124 ). Results: Before enrollment, the majority of pts received only 1 VEGFR- TKI (317 pts, 74%), with 317 pts receiving either SU or SOR (everolimus = 211; placebo = 106) and 99 pts receiving both SU and SOR (everolimus = 66; placebo = 33). Median PFS was 5.42 mo (95% confidence interval [CI]: 4.30, 5.82) in pts receiving everolimus who had received 1 prior VEGFR-TKI and 1.87 mo (95% CI: 1.84, 2.14) in those receiving placebo (hazard ratio [HR]: 0.31; 95% CI: 0.23, 0.42; p < .001). Median PFS was 3.78 mo (95% CI: 3.25, 5.13) for the everolimus group in pts who received 2 prior VEGFR-TKIs, versus 1.87 mo (95% CI: 1.77, 3.06) for the placebo group (HR: 0.37; 95% CI: 0.22, 0.63; p < 0.001). Conclusions: Pts in all stratified subgroups derived significant clinical benefit from everolimus treatment, including pts previously treated with either 1 or 2 VEGFR-TKIs. However, there was a trend toward a longer PFS in pts treated with 1 prior VEGFR-TKI compared with 2 VEGFR-TKIs. [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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".