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Enregistrement W4415556860 · doi:10.1002/jvc2.70206

Skin of Color Representation in Hidradenitis Suppurativa Textbooks

2025· article· en· W4415556860 sur OpenAlexaff
Oswin Chang, Jincheng Shi

Notice bibliographique

RevueJEADV Clinical Practice · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueHidradenitis Suppurativa and Treatments
Établissements canadiensDalhousie UniversityUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésHidradenitis suppurativaRepresentation (politics)Skin lesionStatistical analysisIntertriginousPost-hoc analysisHistopathology

Résumé

récupéré en direct d'OpenAlex

Hidradenitis suppurativa (HS) is a chronic inflammatory skin condition characterized by the presence of abscesses, inflammatory nodules, and scar formation [1]. The prevalence of HS is higher in skin of color, and it presents more severely in these populations [1, 2]. The underrepresentation of darker skin tones in general dermatology textbooks and medical education resources has been well-described [3-5]. Examining skin of color (SoC) representation in condition-specific textbooks is necessary to determine if underrepresentation is also found in subspecialty areas. Therefore, the purpose of this study was to examine SoC representation in HS-specific textbooks. Four HS textbooks were reviewed (Table 1). Textbooks were selected with assistance from a medical librarian, and textbooks with digitally available images were included. Clinical photographs were rated according to the Fitzpatrick skin type (non-SoC were types I-III and SoC were types IV-VI) and the Hurley staging system (I-III), as applicable, and independently verified by a second reviewer. Diagrams, histopathology slides, medical imaging, illustrations, etc. were excluded. Depicted body region and presence of erythema/pigmentary changes was also extracted. SoC representation between textbooks and Hurley stage representation by SoC were compared via chi-squared tests. Statistical significance was determined at p < 0.05 (two-tailed). Statistically significant models were then compared using post hoc chi-squared tests (2 × 2 contingency tables) or Fisher's exact test. Statistical analyses were performed using SPSS Statistics 29 (IBM Corporation). Jemec GBE, Revuz J, Leyden JJ, eds. Hidradenitis Suppurativa. Berlin, Germany: Springer Berlin; 2006. Kimball AB, Jemec GBE, eds. Hidradenitis Suppurativa: A Disease Primer. Cham, Switzerland: Springer International Publishing; 2017. Micali G, ed. Hidradenitis Suppurativa: A Diagnostic Atlas. Hoboken, NJ: John Wiley & Sons; 2017. Shi VY, Hsiao JL, Lowes MA, Hamzavi IH, eds. A Comprehensive Guide to Hidradenitis Suppurativa. Philadelphia, PA: Elsevier; 2021. Note: only this textbook had a chapter dedicated to hidradenitis suppurativa presentation in skin of color In total, there were 460 unique images, of which 272 met inclusion criteria. Two hundred seventeen images (79.8%) were classified as non-SoC, and 55 images (20.2%) were SoC. There were 56 (20.6%) and 114 (41.9%) images for Hurley stages I and II/III, respectively. The most photographed body regions included the axilla (n = 88; 32.4%), inguinal/anogenital region (n = 65; 23.9%), buttock (n = 44; 16.2%), and chest (n = 9; 3.3%). The body region for 21 images (7.7%) was indeterminate. The remaining 45 images (16.5%) came from a variety of locations (i.e., abdomen, arm, back, head/neck, thigh, and multi-region). Erythema was noted in 179 images (65.8%), depigmentation in 28 images (10.3%), and post-inflammatory hyperpigmentation in 141 images (51.8%). There was a statistically significant difference in the proportion of SoC versus non-SoC images between textbooks (p = 0.01; Table 2a). One textbook had greater SoC representation compared to two others. No statistically significant difference was found in the proportion of SoC versus non-SoC images between Hurley stages (p = 0.74; Table 2b). SoC images are proportionally underrepresented overall despite increased disease burden in darker skin tones. Strengths of current resources include broad depiction of various disease stages, inflammatory changes, and body regions. As previously known, SoC underrepresentation is well-documented in general dermatology textbooks, but our findings suggest that underrepresentation extends to condition-specific textbooks as well. There is a continued need to improve representation of darker skin tones within textbook images to reflect global ethnic diversity as countries and populations become increasingly diverse. Accordingly, the emergence of digital image collections has the potential to revolutionize the representation of dermatologic disease in diverse skin tones. A variety of dermatologic journals and societies have curated SoC images to increase awareness of common and historically neglected disease [6]. These online resources are free, crowd-sourced, living visual atlases as opposed to the cross-sectional static depictions in traditional resources like textbooks. Although limitations still exist with regard to image volume and accessibility, these tools are slowly enacting change in the field of ethnic dermatology through technology and collaboration. Oswin Chang: methodology, data curation, investigation, formal analysis, visualization, writing – original draft, writing – review and editing. Jincheng Shi: conceptualization, methodology, data curation, investigation, visualization, writing – review and editing, supervision. The authors have nothing to report. The authors have nothing to report. The authors declare no conflicts of interest. The data that support the findings of this study are available from the corresponding author upon reasonable request.

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)0,011
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
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 enseignants0,490
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,011
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
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 Opus0,059
Tête enseignante GPT0,498
Écart entre enseignants0,439 · 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.

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

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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