Accurately Defining the Incidence and Associations of 90-Day Complications after Urethroplasty: Adverse Impact of Patient Comorbidities, Preoperative Bacteriuria and Prior Urethroplasty
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine the incidence of 90-day complications after urethroplasty and identify factors associated with them. MATERIALS AND METHODS: ), bacteriuria, type of urethroplasty, stricture etiology, length, location, prior endoscopic procedures, previous urethroplasty and preoperative suprapubic catheterization. The primary outcome was the incidence of significant 90-day complications defined as Clavien grade ≥2. Descriptive statistics were used to summarize the results and binary logistic regression was used to examine the factors associated with 90-day complications. RESULTS: Of the 1,611 patients included in the analysis, 90-day complications (Clavien ≥2) occurred in 7.9% (128/1,611) and were wound related (3.5%), urinary tract infection (3.4%), cardiovascular (0.4%), catheter-related (0.2%), hematuria (0.1%) or retention (0.1%). On univariable binary logistic regression stricture location (p=0.04), stricture length (p=0.009), CCI (p <0.0001), prior urethroplasty (p=0.01) and bacteriuria (p=0.002) were associated with complications, while age (p=0.3), etiology (p=0.2), smoking (p=0.2), obesity (p=0.3), failed endoscopic treatment (p=0.8), indwelling suprapubic catheter (p=0.7) and type of urethroplasty (p=0.09) were not. On multivariable analysis, increasing CCI (Odds Ratio 1.31, 95% CI 1.10-1.56; p=0.003), prior urethroplasty (OR 1.86, 95% CI 1.09-3.17; p=0.02) and preoperative bacteriuria (OR 1.67, 95% CI 1.14-2.45; p=0.009) remained associated with 90-day complications. CONCLUSIONS: Patients with increased comorbidities, prior urethroplasty and preoperative bacteriuria are at higher risk for complications after urethroplasty and should be counseled accordingly in a shared decision-making model of care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".