Incidence, Etiology, Prevention and Management of Ureteroenteric Strictures after Robot-Assisted Radical Cystectomy: A Review of Published Evidence and Personal Experience
Bibliographic record
Abstract
Benign ureteroenteric anastomosis strictures (UESs) are one of many critical complications that may cause irreversible disability following robot-assisted radical cystectomy (RARC). Previous studies have shown that the incidence rates of UES after RARC can reach 25.3%, with RARC having higher UES incidence rates compared to open radical cystectomy. Various known and unknown factors are involved in the occurrence of UES. To minimize the incidence of UES after RARC, our group has standardized the procedure and technique for intracorporeal urinary diversion by applying the following five strategies: (1) wide delicate dissection of the ureter and preservation of the periureteral tissues; (2) gentle handling of the ureter and security of periureteral tissues at the anastomotic site; (3) use of indocyanine green to confirm good blood supply; (4) standardization of the ample ureteral spatulation length for Wallace ureteroenteric anastomosis through objective measurements; and (5) development of an institutional standardized procedure manual. This review focused on the incidence, etiology, prevention, and management of UES after RARC to bring attention to the incidence of this complication while also proposing standardized surgical procedures to minimize its incidence after RARC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".