Robotic partial nephrectomy versus radical nephrectomy in elderly patients with large renal masses
Bibliographic record
Abstract
BACKGROUND: Recent evidence suggests that the "oldest old" patients might benefit of partial nephrectomy (PN), but decision-making for this subset of patients is still controversial. Aim of this study is to compare outcomes of robotic partial (RPN) or radical nephrectomy (RRN) for large renal masses in patients older than 65 years. METHODS: We identified 417≥65 years old patients who underwent RRN or RPN for cT1b or ≥cT2 renal mass at 17 high volume centers. Propensity score match analysis was performed adjusting for age, ASA≥3, pre-operative eGFR, and clinical tumor size. Predictors of complications, functional and oncological outcomes were evaluated in multivariable logistic and Cox regression models. RESULTS: After propensity score analysis, 73 patients in the RPN group were matched with 74 in the RRN group. R.E.N.A.L. Score (9.6±1.7 vs. 8.6±1.7; P<0.001), and high complexity (56 vs. 15%; P=0.001) were higher in the RRN. Estimated blood loss was higher in the RPN group (200 vs. 100 mL; P<0.001). RPN showed higher rate of overall complications (38 vs. 23%; P=0.05), but not major complications (P=0.678). At last follow-up, RPN group showed better functional outcomes both in eGFR (55.4±22.6 vs. 45.7±15.7 mL/min; P=0.016) and lower eGFR variation (9.7 vs. 23.0 mL/min; P<0.001). The procedure type was not associated with recurrence free survival (RFS) (HR: 0.47; P=0.152) and overall mortality (OM) (0.22; P=0.084). CONCLUSIONS: RPN in elderly patients with large renal masses provides acceptable surgical, and oncological outcomes allowing better functional preservation relative to RRN. The decision to undergo RPN in this subset of patients should be tailored on a case by case basis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 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".