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Enregistrement W2488279329

New Atrophic Acne Scar Classification: Reliability of Assessments Based on Size, Shape, and Number.

2016· article· en· W2488279329 sur OpenAlexaff
Sewon Kang, Vicente Torres Lozada, Vincenzo Bettoli, Jerry Tan, María José Rueda, Alison Layton, Laurent Petit, Brigitte Dréno

Notice bibliographique

RevuePubMed · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueDermatologic Treatments and Research
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésMedicineScarsAcne scarsDermatologyAcneReliability (semiconductor)Surgery
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Post-acne atrophic scarring is a major concern for which standardized outcome measures are needed. Traditionally, this type of scar has been classified based on shape; but survey of practicing dermatologists has shown that atrophic scar morphology has not been well enough defined to allow good agreement in clinical classification. Reliance on clinical assessment is still needed at the current time, since objective tools are not yet available in routine practice.<br/> OBJECTIVES: Evaluate classification for atrophic acne scars by shape, size, and facial location and establish reliability in assessments.<br/> METHODS: We conducted a non-interventional study with dermatologists performing live clinical assessments of atrophic acne scars. To objectively compare identification of lesions, individual lesions were marked on a high-resolution photo of the patient that was displayed on a computer during the clinical evaluation. The Jacob clinical classification system was used to define three primary shapes of scars 1) icepick, 2) boxcar, and 3) rolling. To determine agreement for classification by size, independent technicians assessed the investigators' markings on digital images. Identical localization of scars was denoted if the maximal distance between their centers was &le; 60 pixels (approximately 3 mm). Raters assessed scars on the same patients twice (morning/afternoon). Aggregate models of rater assessments were created and analyzed for agreement.<br/> RESULTS: Raters counted a mean scar count per subject ranging from 15.75 to 40.25 scars. Approximately 50% of scars were identified by all raters and ~75% of scars were identified by at least 2 of 3 raters (weak agreement, Kappa pairwise agreement 0.30). Agreement between consecutive counts was moderate, with Kappa index ranging from 0.26 to 0.47 (after exclusion of one outlier investigator who had significantly higher counts than all others). Shape classifications of icepick, boxcar, and rolling differed significantly between raters and even for same raters at consecutive sessions (P&lt;.001 and P=0.4, respectively). Analysis showed only 65% of scars were identical in both sessions. We also found that there is a threshold of detection in terms of size, with poor agreement among investigators for very small scars (&lt;2 mm). The repeatability of identification of scars &ge; 2.0 mm was acceptable, and we found that increasing scar size was positively correlated with agreement. Reliability was improved when only scars &gt;2 mm were included. For smaller scars (&lt;2 mm), inter-rater reliability was poor.<br/> CONCLUSIONS: While intuitively it makes sense that describing scar morphology could guide treatment, we have shown that shape-based evaluations are subjective and do not readily yield strong agreement. Until there is a more objective way to evaluate morphology that is readily available to practicing clinicians, we propose that size should be considered a primary characteristic for scar classification systems. We further suggest classification of &lt;2 mm, 2-4 mm, and &gt;4 mm based on how the size would likely affect diagnostic and therapeutic choices. Finally, we recommend that scars &lt;2 mm not be included in a clinical classification but should be evaluated by an objective method that may be refined in the future. <br /><br /> <em>J Drugs Dermatol. </em>2016;15(6):693-702.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,362
Score d'incertitude au seuil0,381

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,051
Tête enseignante GPT0,333
Écart entre enseignants0,282 · 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 tête enseignante, pas un consensus.

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

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

En bref

Citations21
Publié2016
Routes d'admission1
Résumé présentoui

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