Cytoreductive nephrectomy (CN) for metastatic renal cell carcinoma (mRCC) treated with immune checkpoint inhibitors (ICI) or targeted therapy (TT): A propensity score-based analysis.
Bibliographic record
Abstract
608 Background: The role of CN for mRCC treated with ICI is not well defined. Our aim was to evaluate the role of CN for mRCC treated by ICI or TT using a propensity score-based analysis. Methods: We retrospectively assessed patients who were diagnosed with de novo mRCC and who had started first line systemic therapy (ICI or TT) between 2009 and 2019 using the International Metastatic RCC Database Consortium (IMDC). Overall Survival (OS) was compared between patients receiving CN and those treated by systemic therapies alone, using the Kaplan-Meier method and Cox regressions, in the TT and ICI arms separately. In order to account for treatment selection bias, inverse probability of treatment weighting (IPTW) of propensity scores, based on 14 confounding variables, was used and variables were considered balanced if standardized mean difference (SMD) < 0.1. For variables with SMD≥0.1, residual confounding was adjusted for using multivariable models. Results: 3856 patients had been treated by TT (2470 CN+ & 1386 CN-) and 198 by ICI (143 CN+ & 55 CN-). Median follow-up was 38.5 months. After IPTW, baseline characteristics were largely balanced between the CN+ and CN- arms, in the TT and ICI groups (14/14 and 12/14 with SMD < 0.1, respectively). CN was associated with significantly improved OS in both the ICI (Hazard Ratio [HR] = 0.39 [0.19-0.83]) and TT (HR = 0.56 [0.51-0.62]) groups. The interaction term between CN and therapy type (ICI vs TT) was not statistically significant (p = 0.43). The point estimates of the HRs were consistent in sensitivity analyses using multivariable models. Conclusions: In a propensity score-based analysis, CN was found to be associated with a significant OS benefit in patients treated by either ICI or TT. While this study is not a substitute for randomized controlled trials (e.g. CARMENA), the results suggest that CN may still play a role in selected patients in the ICI era.[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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".