Stricture length and etiology as preoperative independent predictors of recurrence after urethroplasty: A multivariate analysis of 604 urethroplasties
Bibliographic record
Abstract
INTRODUCTION: We determine the preoperative identifiable risk factors during staging that predict stricture recurrence after urethroplasty. METHODS: We conducted a retrospective review of all urethroplasties performed at a Canadian tertiary referral centre from 2003 to 2012. Failure was defined as a recurrent stricture <16 Fr on cystoscopic assessment. Multivariate analysis was calculated by Cox proportional hazard regression. RESULTS: In total, 604 of 651 (93%) urethroplasties performed had adequate data with a mean follow-up of 52 months. Overall urethral patency was 90.7% with failures occurring between 2 weeks and 77 months postoperatively. The average time to recurrence was 11.7 months, with most patients with recurrence within 6 months (42/56; 75%). Multivariate regression identified Lichen sclerosus, iatrogenic, and infectious etiologies to be independently associated with stricture recurrence with hazard ratios (HR) (95% confidence interval) of 5.9 (2.1-16.5; p ≤ 0.001), 3.4 (1.2-10; p = 0.02), and 7.3 (2.3-23.7; p ≤ 0.001), respectively. Strictures ≥5cm recurred significantly more often (13.8% vs. 5.9%) with a HR 2.3 (1.2-4.5; p ≤ 0.01). Comorbidities, smoking, previous urethroplasty, stricture location and an age ≥50 were not associated with recurrence. CONCLUSION: Urethroplasty in general is an excellent treatment for urethral stricture with patency rates approaching 91%. While recurrences occur over 6 years after surgery, most (75%) recur within the first 6 months. Long segment strictures (≥5 cm), as well as Lichen sclerosus, infectious and iatrogenic etiologies, are associated with increased risk of recurrence. Limitations include the retrospective, single-centre nature of the study and the 7% loss to follow-up due to the centre being a regional referral one.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".