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Record W4404455317 · doi:10.25251/skin.8.supp.470

Demographics and Disease Characteristics of Patients with Alopecia Areata with Comorbid Atopic Dermatitis, Vitiligo or Anxiety/Depression: TARGET-DERM AA

2024· article· en· W4404455317 on OpenAlexaboutno aff
Natasha Atanaskova Mesinkovska, Sven Richter, Claire Bristow, Ahmed M. Soliman, Julie M. Crawford, Keith Knapp, Breda Muñoz, Lara Wine Lee, Michael Chapman, Amy S. Paller, Benjamin Ungar, Maria Hordinsky

Bibliographic record

VenueSKIN The Journal of Cutaneous Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsnot available
FundersCastle BiosciencesGaldermaIncyteSanofiPfizerConcert PharmaceuticalsEli Lilly and CompanyBristol-Myers Squibb
KeywordsAlopecia areataVitiligoAtopic dermatitisDermatologyDemographicsDepression (economics)MedicineAnxietyPsychiatry

Abstract

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Introduction Alopecia areata (AA) is a chronic autoimmune disease. Common AA comorbidities include atopic dermatitis (AD), vitiligo, anxiety and depression (AnxDep). TARGET-DERM AA is an ongoing longitudinal, real-world study of United States and Canadian AA patients. The purpose of this analysis is to assess characteristics among AA patients with and without the above comorbidities. Methods At enrollment (December 2021-June 2024), patients reported outcomes (including comorbidities, Patient Global Impression of Severity-AA, and clinician-reported outcome measures: Severity of Alopecia Tool, Measure for Eyebrow/Eyelash Hair Loss). Characteristics were compared across subgroups. Results Of the 267 AA patients with completed patient questionnaires at enrollment, 61.4% were female; 24.4% were aged <12 years, 13.7% 12-17, and 61.7% 18 or older. Overall, 21.0% self-reported an AD diagnosis (19.7% of pediatric, 11.1% of adolescent, and 25.4% of adult AA patients), 7.1% vitiligo (7.0% of pediatric, 3.7% of adolescent, and 8.5% of adult AA patients) and 44.9% AnxDep (21.1% of pediatric, 40.7% of adolescent, and 58.5% of adult AA patients). 42.1% of patients with comorbid vitiligo had severe AA disease (SALT>50) and 27.8% patients without comorbid vitiligo had SALT>50. 28.6% of those with comorbid AD had SALT>50 compared to 28.9% of those not reporting AD, with 30.0% of AnxDep and 27.9% of non-AnxDep having SALT>50, all p>0.2. 42.1% of patients with comorbid vitiligo reported PGIS-AA ‘severe/very severe’ disease, with 41.1% of patients with comorbid AD and 42.5% of patients with comorbid AnxDep reporting severe/very severe AA disease, all numerically higher than patients without comorbid vitiligo (36.3%), AD (35.6%), and AnxDep (31.9%), all p>.07. Among those with and without vitiligo, 42.1% vs 37.9% had eyebrow involvement, 36.8% vs 30.2% eyelash; considering AD, 35.7% vs 38.9% had eyebrow involvement, 28.6% vs 31.3% eyelash; for AnxDep 39.2% vs 37.4% had eyebrow involvement, 30.8% v 30.6% eyelash, all p>0.5. Discussion In this real-world cohort of AA patients, the presence of specific comorbidities was not associated with statistically significant differences in clinician reported AA severity, eyebrow or eyelash involvement. Comorbid AnxDep was associated with increased patient-reported AA disease severity. Additional research characterizing how dermatologic and psychiatric comorbidities impact health-related quality of life and patient burden has the potential to inform management decisions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.223
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2024
Admission routes1
Has abstractyes

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