Development and Validation of the Length, Segment, and Etiology Anterior Urethral Stricture Disease Staging System Using Longitudinal Urethroplasty Outcomes Data From the Trauma and Urologic Reconstructive Network of Surgeons
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to create and validate an anterior urethral stricture disease (aUSD) staging system based on the previously validated Length (L), Urethral Segment (S), and Etiology (E; LSE) classification system. MATERIALS AND METHODS: The Trauma and Urologic Reconstructive Network of Surgeons (TURNS) prospective database was used to create and validate the staging system. A novel Urethroplasty Triad Score was created to aid in ranking the stagings into stricture severity based on (1) functional outcomes, (2) location of urethral meatus (eg, orthotopic, perineal), and (3) number of surgeries required for repair. Staging was secondarily validated in a non-TURNS dataset and then compared with 2 previously described aUSD severity scores-the U-score and the LSE score. RESULTS: Five aUSD stages, with 10 total substages, were ultimately created: stage I-short bulbar, stage II-long bulbar, stage III-penile/fossa of favorable etiology, stage IV-penile/fossa of adverse pathology, and stage V-pan-urethral (3-segment). Mean Urethroplasty Triad Score decreased (increasing severity) with each substage, with the linear trend being validated in both the separate validation cohort and within the individual TURNS. LSE staging was superior to the LSE score and U-score in predicting the need for multiple stages or a nonorthotopic meatus and was similar in predicting surgical outcomes. CONCLUSIONS: Each stage and substage of this novel LSE staging system was shown to provide unique information on stricture characteristics, repairs, and surgical outcomes. The LSE staging system will improve communication of stricture complexity/severity with our patients and organize aUSD for multi-institutional outcomes studies and clinical trial recruitment purposes.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 teacher head, 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".