Comparison of First-Line Anti-PD-1-Based Combination Therapies in Metastatic Renal-Cell Carcinoma: Real-World Experiences from a Retrospective, Multi-Institutional Cohort
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Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to test for differences in overall (OS) and progression-free survival (PFS) rates and toxicity in first-line immune checkpoint inhibition (IO) combination therapy in metastatic renal-cell carcinoma (mRCC) patients. METHODS: Between November 2017 and April 2021, 104 patients with histologically confirmed mRCC from 6 tertiary referral centers with either IO + IO (nivolumab + ipilimumab, n = 68) or IO + tyrosine kinase inhibitor (TKI) (pembrolizumab + axitinib, n = 36) were included. Kaplan-Meier and Cox regression analyses tested for OS and PFS differences. RESULTS: Of 104 mRCC patients, 68 received IO + IO (65.4%) and 36 IO + TKI (34.6%) therapy, respectively. Median age was 67 years (interquartile range: 57-70.3). Patients receiving IO + TKI were less likely to be poor risk according to the International Metastatic Renal-Cell Carcinoma Database Consortium score (16.7 vs. 30.9%) and presented with lower T-stage, compared to IO + IO treated patients. Median PFS was 9.8 months (CI: 5.3-17.6) versus 12.3 months (CI: 7.7 - not reached) for IO + IO versus IO + TKI treatment, respectively (p = 0.22). Median OS was not reached, survival rates at 12 months being 73.9 versus 90.0% for IO + IO versus IO + TKI patients (p = 0.089). In subgroup analyses of elderly patients (≥70 years, n = 38), IO + TKI treatment resulted in better OS rates at 12 months compared to IO + IO (91.0 vs. 57.0%; p = 0.042). CONCLUSION: IO + IO and IO + TKI as first-line therapies in mRCC patients were both comparable as for the oncological outcome and toxicity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 it