The UREThRAL stricture score: A novel method for describing anterior urethral strictures
Bibliographic record
Abstract
BACKGROUND: : Urethral stricture description is not standardized. This makes surgical decision-making less reproducible and increases the difficulty of objectively analyzing urethroplasty literature. We developed a standardized system, the UREThRAL stricture score (USS), to quantify the characteristics of anterior urethral stricture disease based on preoperative imaging and intraoperative findings. METHODS: : To develop the USS, we retrospectively analyzed 95 consecutive patients with urethral strictures who underwent open urethroplasty by a single surgeon (SBB) at Barnes-Jewish Hospital from 2009 to 2011. The USS is a numerical score based on five components of anterior urethral stricture disease that help dictate operative decision-making: (1) (UR)ethral stricture (E)tiology; (2) (T) otal number of strictures; (3) (R)etention (luminal obliteration); (4) (A)natomic location; and (5) (L)ength. Stricture management was categorized by increasing surgical complexity: excision/primary anastomosis (EPA), buccal mucosal graft urethroplasty (BMG), augmented anastomotic urethroplasty (AAU), flap urethroplasty, and a combination of flaps and/or grafts. Multinomial logistic regression analysis was used to compare USS to surgical complexity. RESULTS: : The mean USS for EPA, BMG, AAU, flap, and combination flaps/grafts was 5.78, 8.82, 9.23, 11.01, and 14.97, respectively. Increasing USS was significantly associated with surgical complexity (p < 0.0001). INTERPRETATION: : The USS describes the essential factors in determining surgical treatment selection for urethral stricture disease. The USS is a concise, easily applicable system that delineates the clinically significant features of urethral strictures. Valuable comparison of anterior urethral stricture treatments in clinical practice and in the urological literature could be facilitated by using this novel UREThRAL stricture score.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".