Impact of body mass index on perioperative outcomes during the learning curve for robot assisted radical prostatectomy
Bibliographic record
Abstract
INTRODUCTION: Previous studies of robotic-assisted radical prostatectomy (RARP) have suggested that obesity is a risk factor for worse perioperative outcomes. We evaluated whether body mass index (BMI) adversely affected perioperative outcomes. METHODS: A prospective database of 153 RARP (single surgeon) was analyzed. Obesity was defined as BMI >/= 30 kg/m(2); normal BMI < 25 kg/m(2); and overweight as 25 to 30 kg/m(2). Two separate analyses were performed: the first 50 cases (the initial learning curve) and the entire cohort of 153 RARP. RESULTS: In the initial cohort of 50 cases (14 obese patients), there was no statistically significant difference with regards to operative times, port-placement times and estimated blood loss (EBL). Length of stay (LOS) was longer in the obese group (4.3 vs. 2.9 days); BMI remained an independent predictor of increased LOS on multivariate linear regression analysis (p = 0.002). There was no statistically significant difference in the postoperative outcomes of leak rates, margin rates and incisional herniae. In the entire cohort, when comparing obese patients to those with a normal BMI, there was no statistically significant difference in operative times, EBL, LOS, or immediate postoperative outcomes. However, on multivariate linear regression analysis, BMI was an independent predictor of increased operative time (p = 0.007). CONCLUSION: Obese patients do not have an increased risk of blood loss, positive margins or the postoperative complications of incisional hernia and leak during the learning curve. They do, however, have slightly longer operative times; we also noted an increased LOS in our first 50 cases.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".