Impact of Robotic-Assisted Partial Nephrectomy with Single Layer versus Double Layer Renorrhaphy on Postoperative Renal Function
Bibliographic record
Abstract
We aimed to investigate the differences in renal function between patients who underwent single inner-layer renorrhaphy (SILR) or double-layer renorrhaphy (DLR) among those with renal tumors who underwent robot-assisted partial nephrectomy (RAPN). This retrospective multicenter cohort study was conducted between November 2018 and October 2023 at two institutions and included patients who underwent RAPN. In total, 93 eligible patients who underwent RAPN were analyzed. Preoperative renal function and prevalence of chronic kidney disease were not significantly different between the two groups. Although urinary leakage was observed in three patients (5.9%) in the SILR group, there was no significant difference between the two groups regarding surgical outcomes (p = 0.249). Serum creatinine levels after RAPN were significantly lower in the SILR group than in the DLR group on postoperative days 1 and 365 following RAPN (p = 0.04). The estimated glomerular filtration rate (eGFR) was significantly lower in the DLR group than in the SILR group only on postoperative day 1; however, there was no significant difference between the two groups thereafter. Multivariate analysis showed that the method of renorrhaphy was not a predictor for maintaining renal function after RAPN even though it was associated with eGFR on postoperative day 1.
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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".