Excision and Primary Anastomosis for Short Bulbar Strictures: Is It Safe to Change from the Transecting towards the Nontransecting Technique?
Bibliographic record
Abstract
OBJECTIVE: To explore whether it is safe to change from transecting excision and primary anastomosis (tEPA) towards nontransecting excision and primary anastomosis (ntEPA) in the treatment of short bulbar urethral strictures and to evaluate whether surgical outcomes are not negatively affected after introduction of ntEPA. MATERIALS AND METHODS: Two-hundred patients with short bulbar strictures were treated by tEPA (n=112) or ntEPA (n=88) between 2001 and 2017 in a single institution. Failure rate and other surgical outcomes (complications, operation time, hospital stay, catheterization time, and extravasation at first cystography) were calculated for both groups. Potentially predictive factors for failure (including ntEPA) were analyzed using Cox regression analysis. RESULTS: Median follow-up for the entire cohort was 76 months, 118 months, and 32 months for, respectively, tEPA and ntEPA (p<0.001). Nineteen (9.5%) patients suffered a failure, 13 (11.6%) with tEPA and 6 (6.8%) with ntEPA (p=0.333). High-grade (grade ≥3) complication rate was low (1%) and not higher with ntEPA. Median operation time, hospital stay, and catheterization time with tEPA and ntEPA were, respectively, 98 and 87 minutes, 3 and 2 days, and 14 and 9 days. None of these outcomes were negatively affected by the use of ntEPA. Diabetes and previous urethroplasty were significant predictors for failure (Hazard ratio resp. 0.165 and 0.355), whereas ntEPA was not. CONCLUSIONS: Introduction of ntEPA did not negatively affect short-term failure rate, high-grade complication rate, operation time, catheterization time, and hospital stay in the treatment of short bulbar strictures. Diabetes and previous urethroplasty are predictive factors for failure.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".