<p>Predictors of postoperative complications after robot-assisted radical cystectomy with extracorporeal urinary diversion</p>
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Bibliographic record
Abstract
Purpose: Robot-assisted radical cystectomy (RARC) is known to have less postoperative morbidity and complications than open radical cystectomy. However, various complications not yet have been reported after RARC. In this study, we aimed to identify the predictors of complications following RARC. Patients and methods: From August 2008 to November 2017, we retrospectively reviewed 126 patients who underwent RARC with extracorporeal urinary diversion. Overall perioperative complications were examined, and factors that may affect complications were analyzed using a logistic regression model. Complications were classified according to the Clavien-Dindo system. Results: Overall postoperative complications occurred in 78 (61.9%) of 126 patients. Whereas the rate of minor complications was 58.0% (grade I=15.9% (n=20), grade II=42.1% (n=53)), the rate of major complications was very low (grade IIIa=1.6% (n=2), grade IIIb =2.4% (n=3)). No fatal complications more than grade IV were developed. Notably, transfusions (27.0%), urinary tract infection (15.9%), anastomosis site leakage (14.3%), and ileus (10.3%) were the most common complications after RARC. In the multivariate regression model, previous intravesical instillation (odds ration [OR]=3.374), preoperative hemoglobin (OR=0.751), and estimated blood loss (EBL) (OR=3.949) were identified as the predictors of postoperative complications. Conclusion: In sum, our data showed the rates of major complications were comparable after RARC with extracorporeal urinary diversion compared as reported in previous studies and lower major than minor complications following RARC. Moreover, we identified the independent predictors of postoperative complications, such as preoperative hemoglobin, intravesical instillation, and EBL. Keywords: bladder cancer, complications, cystectomy, predictor, robotic surgery
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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) | 0.001 | 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 it