Is anastomotic urethroplasty is really superior than BMG augmented dorsal onlay urethroplasty in terms of outcomes and patient satisfaction: Our 4-year experience
Bibliographic record
Abstract
INTRODUCTION: We analyzed the outcomes of augmented buccal mucosa graft (BMG) dorsal onlay urethroplasty and anastomotic urethroplasty in the management of urethral stricture. METHODS: Patients having a stricture length more than 2 cm were treated by augmented BMG dorsal onlay urethroplasty; patients with a stricture length less than 2 cm were managed by excision and end-to-end anastomotic urethroplasty. The postoperative retrograde urethrogram, micturating cystourethrogram, and uroflowmetry were compared to preoperative values. The postoperative subjective symptoms and complications were recorded and analyzed. RESULTS: In total, 90 patients were included in this study. Forty-five patients had an average stricture length of 5.9 cm; they underwent BMG augmented dorsal onlay urethroplasty. Of these, 7 (15.55%) patients came with recurrence, while 38 (84.44%) were asymptomatic, in the average follow-up period of 32.8 months. The next 45 patients underwent excision of the stricture and end-to-end anastomosis. Of these, 6 (13.33%) failed on therapy and the remaining 39 (86.66%) were asymptomatic during the average follow-up period of 28.4 months. CONCLUSION: The technique of BMG dorsal onlay is easy to do, it is very reliable, has high success rate, less postoperative complications and better patient satisfaction compared to anastomotic urethroplasty. Our study has its limitations. Recurrent cases of urethroplasty and hypospadias were excluded from this study. Recurrent stricture cases were eliminated to overcome bias. Cases of hypospadias are still best treated by axial or random penile skin flap as BMG augmentation cannot create a long urethral tube. Based on our 4-year experience, we recommend BMG augmented urethroplasty long and short segment stricture of the urethra.
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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 | 0.002 |
| 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 | 0.000 |
| 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".