The UREThRAL stricture score: A novel method for describing
Bibliographic record
Abstract
Background: Urethral stricture description is not standardized. Thismakes surgical decision-making less reproducible and increasesthe difficulty of objectively analyzing urethroplasty literature. Wedeveloped a standardized system, the UREThRAL stricture score(USS), to quantify the characteristics of anterior urethral stricturedisease based on preoperative imaging and intraoperative findings.Methods: To develop the USS, we retrospectively analyzed 95consecutive patients with urethral strictures who underwent openurethroplasty by a single surgeon (SBB) at Barnes-Jewish Hospitalfrom 2009 to 2011. The USS is a numerical score based on fivecomponents of anterior urethral stricture disease that help dictateoperative 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 wascategorized by increasing surgical complexity: excision/primaryanastomosis (EPA), buccal mucosal graft urethroplasty (BMG), augmented anastomotic urethroplasty (AAU), flap urethroplasty, and acombination of flaps and/or grafts. Multinomial logistic regressionanalysis was used to compare USS to surgical complexity.Results: The mean USS for EPA, BMG, AAU, flap, and combinationflaps/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 determiningsurgical treatment selection for urethral stricture disease.The USS is a concise, easily applicable system that delineates theclinically significant features of urethral strictures. Valuable comparison of anterior urethral stricture treatments in clinical practiceand 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 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".