Robot-assisted laparoscopic ureteral reconstruction for ureter endometriosis: Case series and literature review
Bibliographic record
Abstract
BACKGROUND: The aim of this report was to review experience from a single hospital in treating ureteral obstruction related to endometriosis with robot-assisted laparoscopic ureteral reconstruction. METHODS: This retrospective analysis study (Canadian Task Force classification II-3) was conducted at an academic tertiary hospital. Five female patients with hydronephrosis without significant elevation of serum creatinine levels were enrolled. Ureteral endometriosis with obstruction was suspected on radiological images. Previous treatment with double-J stenting with or without medical treatment had failed in all of the patients. We performed robot-assisted laparoscopic segmental resection for ureteral endometriosis and reconstructed the ureter through ureteroureterostomy (RUU) or ureteroneocystostomy (RUC). The involved ureters included left lower ureter in three patients and right lower ureter in two patients. RUU was performed in four patients and RUC in one patient. All of the operations were completed smoothly without complications. RESULTS: All ureteral endometrioses were successfully resected, and follow-up sonography or intravenous pyelography showed resolution of hydronephrosis in all of the patients. CONCLUSION: Our experience proves the feasibility and efficacy of a robot-assisted approach for this rare situation with good outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".