Complete metastasectomy in renal cell carcinoma: a propensity-score matched by the International Metastatic RCC Database Consortium prognostic model
Bibliographic record
Abstract
INTRODUCTION: We evaluated overall survival (OS) benefit of complete metastasectomy (CM) in metastatic renal cell carcinoma (mRCC) using a propensity score-matched (PSM) analysis to balance groups by age, gender and by the International Metastatic RCC Database Consortium prognostic model (IMDC). METHODS: We included patients (pts) treated at the AC Camargo Cancer Center between 2007 and 2016. Pairs were matched by age, gender and IMDC. Kaplan-Meier survival estimates and Cox proportional hazard models were used to evaluate OS on CM and no-CM group. RESULTS: We found 116 pts with clear cell mRCC. After PSM, the number was reduced to 74 (37 CM, 37 no-CM). The median OS for CM and no-CM was 98.3 months and 40.5 months, respectively (hazard ratio 0.24 95%CI 0.11-0.53 p < 0.001). The OS benefit of CM was confirmed on favourable and intermediate IMDC but was absent on poor IMDC. The CM group received less systemic therapy than the no-CM group. Ten pts in the CM group still have no evidence of disease (NED). CONCLUSION: After matching for age, gender and IMDC, we found CM impacts on OS and also diminishes the need for systemic treatment. Survival benefit was confirmed for favourable/intermediate IMDC but not for the poor IMDC prognostic model. Further studies correlating IMDC and metastasectomy are needed to guide clinical decision-making.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".