Robotic partial nephrectomy for renal tumours in obese patients: Perioperative outcomes in a multi-institutional analysis
Bibliographic record
Abstract
INTRODUCTION: We sought to evaluate the association of obesity with surgical outcomes of robotic partial nephrectomy (RPN) using a large, multicentre database. METHODS: We identified 1836 patients who underwent RPN from five academic centres from 2006-2014. A total of 806 patients were obese (body mass index [BMI] ≥30 kg/m(2)). Patient characteristics and outcomes were compared between obese and non-obese patients. Multivariable analysis was used to assess the association of obesity on RPN outcomes. RESULTS: A total of 806 (44%) patients were obese with median BMI of 33.8kg/m(2). Compared to non-obese patients, obese patients had greater median tumour size (2.9 vs. 2.5cm, p<0.001), mean RENAL nephrometry score (7.3 vs. 7.1, p=0.04), median operating time (176 vs. 165 min, p=0.002), and median estimated blood loss (EBL, 150 vs. 100 ml, p=0.002), but no difference in complications. Obesity was not an independent predictor of operative time or EBL on regression analysis. Among obese patients, males had a greater EBL (150 vs. 100 ml, p<0.001), operative time (180 vs. 166 min, p<0.001) and warm ischemia time (WIT, 20 vs. 18, p=0.001), and male sex was an independent predictor of these outcomes on regression analysis. CONCLUSIONS: In this large, multicentre study on RPN, obesity was not associated with increased complications and was not an independent predictor of operating time or blood loss. However, in obese patients, male gender was an independent predictor of greater EBL, operative time, and WIT. Our results indicate that obesity alone should not preclude consideration for RPN.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".