Investigating the Outcomes and Complications of Urethroplasty Using Different Graft Materials in Men With Complex or Recurrent Urethral Strictures
Bibliographic record
Abstract
Background Complex and recurrent urethral strictures present significant surgical challenges, with graft-based urethroplasty being the mainstay of treatment. Optimal graft selection remains controversial, particularly between buccal mucosa and penile skin grafts. Objective To compare clinical outcomes, complication rates, and patient-reported satisfaction following urethroplasty using buccal mucosa versus penile skin grafts. Methods This retrospective cohort study was conducted at Nishtar Hospital, Multan, Pakistan, from January 2022 to December 2024. A total of 178 male patients who underwent substitution urethroplasty were included in the study. Data were retrospectively extracted from electronic medical records and surgical logs. Collected variables included patient demographics (age, comorbid conditions like diabetes or smoking status), stricture characteristics (location, length, etiology, previous treatments), surgical details (graft type, operative time, perioperative complications), and postoperative outcomes. Data were analyzed using SPSS Version 26.0 (IBM SPSS Statistics for Windows, IBM Corp., Armonk, NY). Continuous variables were compared using t-tests and categorical variables using chi-square tests, with p < 0.05 considered statistically significant. Results Surgical success rates were higher in the buccal mucosa group at both 12 months (91 (87.5%) vs. 59 (80.2%)) and 24 months (88 (84.6%) vs. 56 (75.6%)), though differences were not statistically significant. Stricture recurrence and graft contracture were lower in the buccal mucosa group (11 (10.6%) vs. 14 (18.9%)) and (four (3.8%) vs. eight (10.8%)), respectively. International Prostate Symptom Score (IPSS) improvement was significantly greater with buccal mucosa grafts (p = 0.04), and patient satisfaction rates were also higher. Complication rates were low in both groups, but oral donor site morbidity was observed in 19 (18%) of buccal mucosa patients. Conclusions It is concluded that buccal mucosa grafts offer superior functional outcomes, lower complication rates, and greater patient satisfaction compared to penile skin grafts in the setting of complex or recurrent urethral strictures. Buccal mucosa should be considered the preferred graft material when feasible.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".