Transperitoneal laparoscopic nephrectomy: Assessing complication risk in cases of previous abdominal surgery
Bibliographic record
Abstract
Introduction: We aimed to assess the effect of previous abdominal surgery on perioperative outcomes in patients undergoing transperitoneal laparoscopic partial (LPN) or radical (LRN) nephrectomy for renal masses.Methods: We retrospectively reviewed all cases of LPN and LRN for renal masses at our institution between 2008 and 2014. Patients were divided in two groups, those with and without prior abdominal surgery. Four perioperative outcomes were compared, namely, operative time (OT), estimated blood loss (EBL), length of stay (LOS), and 30-days complications rate. A subanalysis was performed to address the impact of previous open cholecystectomy on right LPN or LRN.Results: Of 293 patients identified, 146 (49.8%) had previous abdominal surgery. In univariate analysis, no differences in operative time (136 vs. 144 minutes; p=0.154), EBL (88 vs. 100 mL; p=0.211), or 30-day complication rate (24 vs. 14%; p=0.069) were recorded between the groups. Only LOS favoured patients without previous abdominal surgery (3 vs. 4 days; p=0.001). In multivariate analysis, prior abdominal surgery was not associated with an increased OT, EBL, LOS, or complication rate. The analysis of right nephrectomies showed increased OT (148 vs. 128 minutes; p=0.049) and complication rate (42 vs. 16%; p=0.004) for patients with past open cholecystectomy compared to those without. Multivariate analysis revealed that prior open cholecystectomy was associated with a longer LOS (ORmedian=2.7 [1.2‒8.0]) and an increased risk of complications (ORmedian=4.5 [1.6‒10.5]).Conclusions: In this cohort, previous abdominal surgery was not associated with worse perioperative outcomes after transperitoneal LPN and LRN for renal masses. However, previous open cholecystectomy resulted in a higher risk of complication and a longer LOS in patients undergoing right laparoscopic nephrectomy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".