Response to Letter to Editor: “Development and Validation of a Male Anterior Urethral Stricture Classification System”
Bibliographic record
Abstract
We thank Dr Brandes and colleagues for taking such an interest in the LSE classification system and hope that others will take the time to understand its utility as much as they have. We also acknowledge the effort that he and others have taken into development of their U-score and will again recognize the similarities between the 2 systems here, as we did in the original manuscript. However, describing a urethral stricture based on its location, length, and etiology is hardly novel. There is nothing proprietary about saying “this patient has a 1 cm, proximal bulbar urethral stricture caused by a straddle injury”; or “this patient has a 2 cm, meatal stricture secondary to prolonged catheterization.” But the difference in how each respective classification/scoring system would categorize these strictures speaks to how they can and should be used in clinical practice. The U-score would give both strictures a score of “7”–this based on point totals for length, location and etiology in nonobliterative strictures. This number should tell the surgeon that historically, both strictures should be easy to manage with good outcomes. Alone, however, the number is unable to identify where the stricture is located, how long it is, what caused it, or even how it would be managed. For that information, you'll need a classification system. The LSE classification system would classify these strictures as L1S1aE1 and L1S2dE3a, respectively. While the LSE system is certainly more “complex,” the complexity only lies in the way it labels, and standardizes, the way we already talk about and describe strictures. When developing the system, we went to great lengths to ensure that each subcategory of length, location and etiology were clearly distinct from one another, were reproducible, and were easy to understand (validation step 1). We then ensured that once the stricture was classified, it would help predict urethroplasty type, potentially aiding the surgeon clinically (validation step 2). The third logical step, which is correctly identified by Dr. Brandes as being absent from this manuscript, would be to validate clinical outcomes. This exciting step is ongoing, but to preview how novel the findings from such a study could be, it will now be theoretically possible to study the outcomes of 168+ different types of strictures (7 locations × 3 lengths × 8 etiologies). Notably, many of those combinations will be rare and clinically insignificant–but we hope to ultimately determine that each stricture type indeed has a best way to manage it. The validated LSE classification system will serve as the tool, and starting point, to figure that out methodically and systematically. To address the difficulty of use, as with any new system, it will need to be learned and practiced. Anecdotally, its incorporation into busy urethral stricture clinical practices has been common amongst residents and fellows associated with its development–and it is already a part of our own practices. However, an online classification tool will also be posted at www.turnsresearch.org to help with the transition. Letter re: Erickson BA et al: Development and validation of a Male Anterior Urethral Stricture Classification SystemUrologyVol. 147PreviewWe read Erickson et al1 with interest. We agree with the authors that a validated anterior urethral stricture classification system is most helpful when it can “predict clinical outcomes, aid in clinical communication, and facilitate (comparative) research.” The U Score, which we published in 2015, is such a stricture classification system. The detailed TURNS LSE classification system is based on the 3 pillars of stricture length, location, and etiology (LSE). We published in 2012 the first descriptive anterior urethral stricture classification system, called UREThRAL score,2 which we refined later into a categorical grading scale called the U score. Full-Text PDF
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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 | 0.005 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.021 | 0.024 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".