Feasibility of laparoscopic partial nephrectomy in the obese patient and assessment of predictors of perioperative outcomes
Bibliographic record
Abstract
Introduction: Partial nephrectomy is the gold standard for treatment of small renal masses. Our study compares outcomes for obese (body mass index [BMI] ≥30) and healthy (BMI <30) patients undergoing laparoscopic partial nephrectomy (LPN) with the intention of defining preoperative risk factors for complications and renal insufficiency in the obese. Materials and Methods: We conducted a retrospective review of 187 consecutive patients who underwent LPN. We examined the association between BMI and postoperative complication, estimated blood loss (EBL), hospital length of stay, warm ischemic time (WIT), and postoperative renal function. We did similar analyses using the RENAL nephrometry score and the comorbidity status of the patients. Results: We found no statistically significant increase in complications in obese (BMI ≥30) individuals relative to healthy (BMI <30) patients. The obese experienced approximately 100 cc more EBL (P = 0.0111). Patients experienced more complications if they had a Charlson comorbidity score ≥3 (P = 0.0065), an American Association of Anesthesiologists score ≥3 (P = 0.0042), or a history of diabetes mellitus (P = 0.0196). There was no association between RENAL nephrometry score and complication. However, patients with a score ≥8 experienced higher WIT (P = 0.0022), a greater decline in estimated glomerular filtration rate postoperatively (P = 0.0488), and an increased risk of developing chronic kidney disease ≥3 (P = 0.0065). Conclusions: Obese patients undergoing LPN are not at significantly increased risk of complication relative to nonobese patients. Comorbidity status and RENAL nephrometry score, independent of BMI, should be the main considerations of a patient's suitability for LPN.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".