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Enregistrement W3097441441 · doi:10.1016/j.urology.2020.10.028

Response to Letter to Editor: “Development and Validation of a Male Anterior Urethral Stricture Classification System”

2020· letter· en· W3097441441 sur OpenAlexaff
Bradley A. Erickson, Kevin J. Flynn, Amy E. Hahn, Katherine Cotter, Nejd F. Alsikafi, Benjamin N. Breyer, Joshua A. Broghammer, Jill C. Buckley, Sean P. Elliott, Jeremy B. Myers, Andrew C. Peterson, Keith Rourke, Thomas G. Smith, Alex J. Vanni, Bryan B. Voelzke, Lee C. Zhao

Notice bibliographique

RevueUrology · 2020
Typeletter
Langueen
DomaineMedicine
ThématiqueUrological Disorders and Treatments
Établissements canadiensUniversity of Alberta
Organismes subventionnairesNational Institute of Diabetes and Digestive and Kidney Diseases
Mots-clésMedicineEtiologyUrethral strictureCategorizationNothingSurgeryArtificial intelligenceInternal medicineUrethra

Résumé

récupéré en direct d'OpenAlex

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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,053
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,021
Score d'incertitude au seuil0,029

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,053
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,002
Communication savante0,0030,003
Science ouverte0,0030,001
Intégrité de la recherche0,0210,024
Charge utile insuffisante (le modèle a refusé de juger)0,0090,009

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,023
Tête enseignante GPT0,261
Écart entre enseignants0,238 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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