Clinical outcomes in patients with metastatic renal cell carcinoma receiving everolimus or temsirolimus after sunitinib.
Bibliographic record
Abstract
INTRODUCTION: There are little data on the clinical activity of temsirolimus (TM) and everolimus (EV) when used as second-line therapy after sunitinib (SU) in patients with metastatic renal cell carcinoma (mRCC). METHODS: Patients with mRCC treated with EV or TM after SU were included in this retrospective analysis. Progression-free survival (PFS), time to sequence failure (TTSF) from the start of SU to disease progression with EV/TM and overall survival (OS) were estimated using Kaplan-Meier method and compared across groups using the log-rank test. Cox proportional hazards models were applied to investigate predictors of TTSF and OS. RESULTS: In total, 89 patients (median age 60.0 years) were included. At baseline 43% were classified as MSKCC good-risk, 43% as intermediate-risk and 14% as poor-risk. Median OS was 36.3 months and median TTSF was 17.2 months. Sixty-five patients received SU-EV and 24 patients SU-TM. Median PFS after the second-line treatment was 4.3 months in the EV group and 3.5 months in the TM group (p = 0.63). Median TTSF was 17.0 and 18.9 months (p = 0.32) and the OS was 35.8 and 38.3 months (p = 0.73) with SU-EV and SU-TM, respectively. The prognostic role of initial MSKCC was confirmed by multivariable analysis (hazard ratio 1.76, 95% confidence interval 1.08-2.85. p = 0.023). CONCLUSIONS: This study did not show significant differences in terms of disease control and OS between EV and TM in the second-line setting. EV remains the preferred mTOR inhibitor for the treatment of mRCC patients resistant to prior tyrosine kinase inhibitor treatment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".