Durability of Laser Endoureterotomy for the Treatment of Benign Ureteral Strictures: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Purpose: Laser endoureterotomy is a common minimally invasive treatment of ureteral strictures. However, data evaluating the durability of this technique are limited. This systematic review and meta-analysis assessed treatment success rates after laser endoureterotomy for managing benign ureteral strictures. Materials and Methods: A systematic literature search identified studies published from January 2000 to December 2023 reporting outcomes of laser endoureterotomy for benign ureteral strictures. Random-effects meta-analysis models calculated treatment success rates at 3 months, 6 months, and annually thereafter until estimates became unreliable (standard error > 10%). Results: Nine studies comprising 308 patients (310 strictures) met eligibility criteria. The methodological quality of the studies was rated as good in 8 studies and fair in 1 study. Strictures were most commonly attributable to stone disease or previous surgery, predominantly located in the proximal (38%) or distal (35%) ureter, with a median length of 10 mm. Holmium laser was used in 98% of cases. Intraoperative complication rates were low (0.9%). The treatment success rate was 84% at 3 months, 74% at 1 year, 70% at 2 years, and 68% at 3 years. Sensitivity analysis indicated that the results were not overly influenced by any single study, and modest heterogeneity was observed among studies. Conclusions: Minimally invasive laser endoureterotomy is an effective and durable treatment for short benign ureteral strictures, making it a viable alternative to reconstructive surgery.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.039 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".