Stricture length and etiology as preoperative independent predictors of recurrence after urethroplasty: A multivariate analysis of 604 urethroplasties
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 it