Robot-assisted laparoscopic continent cutaneous urinary diversion in a single- center study; surgical technique and outcomes
Bibliographic record
Abstract
Abstract Introduction: Robot assisted laparoscopic cutaneous continent urinary diversion (RALCCUD) has been shown to be feasible; however, data on clinical outcomes in adults are lacking. Materials & Methods: We conducted a retrospective study of all adults who underwent RALCCUD between 2017 and 2022 at a single tertiary reference center. Participant characteristics, clinical information and perioperative outcomes were recorded. All participants underwent pre and postoperative urodynamic evaluations. Functional outcomes were evaluated at 3 months, then yearly. Continence was defined as no stomal or urethral leakage. Results: Twelve patients, mostly women (n=11), median (IQR) age 47.4 (19-57) years underwent RALCCUD (4 Mitrofanoff, 4 Yang-Monti and 4 Casale). The main indication for surgery was inability to perform intermittent self-catheterization through the native urethra. Eleven patients (92%) had neurogenic lower urinary tract disease caused by spinal cord injury or spinal dysraphism. Median (IQR) operative time was 313 (285-367) min. Four patients (33%) underwent concomitant procedures: 3 supra-trigonal cystectomy with augmentation cystoplasty and 1 artificial urinary sphincter. No conversions to an open approach were required. Median (IQR) follow-up was 42.9 (34-53) months. One early postoperative complication occurred (Clavien grade III). The late postoperative complication rate was 17%, with 3 complications occurring in 2 patients. At the last follow up, all patients could self-catheterize through the tube, and the stomal and urethral continence rate was 100%. Conclusion: RALCCUD is feasible and safe in adults, with a high rate of stomal and urethral continence and a low complication rate.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".