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Record W4415556860 · doi:10.1002/jvc2.70206

Skin of Color Representation in Hidradenitis Suppurativa Textbooks

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

Bibliographic record

VenueJEADV Clinical Practice · 2025
Typearticle
Languageen
FieldMedicine
TopicHidradenitis Suppurativa and Treatments
Canadian institutionsDalhousie UniversityUniversity of British Columbia
Fundersnot available
KeywordsHidradenitis suppurativaRepresentation (politics)Skin lesionStatistical analysisIntertriginousPost-hoc analysisHistopathology

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.498
Teacher spread0.439 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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