Differences in other-cause mortality in metastatic renal cell carcinoma according to partial vs. radical nephrectomy and age: A propensity score matched study
Bibliographic record
Abstract
INTRODUCTION: It is unknown whether the benefit from partial nephrectomy regarding lower other-cause mortality is applicable to older patients with metastatic renal cell carcinoma. MATERIALS AND METHODS: Using Surveillance Epidemiology and End Results database, patients with metastatic renal cell carcinoma, undergoing partial or radical nephrectomy, were stratified according to age (<60, 60-69, and ≥70 years). After propensity score matching, Kaplan-Meier survival analyses and multivariable Cox regression models were used. RESULTS: Of 2,390 patients with metastatic renal cell carcinoma, 885 (37%) were aged <60 years, and 90 (10%) underwent partial nephrectomy; 824 (34%) were aged 60-69 years, and 61 (7%) underwent partial nephrectomy; and 681 (29%) were aged ≥70 years, and 64 (9%) underwent partial nephrectomy. After propensity score matching, in patients aged <60 years, partial nephrectomy was associated with lower other-cause mortality (hazard ratio 0.22; p = 0.02); in patients aged 60-69 years, partial nephrectomy was associated with lower other-cause mortality (hazard ratio 0.38; p = 0.03); but not in patients aged ≥70 years. DISCUSSION: In metastatic renal cell carcinoma, partial nephrectomy is associated with lower other-cause mortality in patients aged <60 years and in patients aged 60-69 years, but not in patients aged ≥70 years. In consequence, consideration of partial nephrectomy might be of great value in younger metastatic renal cell carcinoma patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 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".