Race and Ethnicity Sub-Groups of Alopecia Areata Patients have Differing Clinical Characteristics: TARGET-DERM AA
Bibliographic record
Abstract
Introduction Alopecia areata (AA) is a chronic, autoimmune disease that disproportionately impacts particular subgroups.1 Methods United States and Canadian clinics enrolled participants in the TARGET-DERM AA registry (December 2021 - June 2024, data collection ongoing). Those who completed a baseline patient questionnaire [self-reported race / ethnicity, Patient Global Impression of Severity (PGIS-AA)] and clinician-reported outcome measures [Severity of Alopecia Tool (SALT), ClinRO Measure for Eyebrow / Eyelash Hair Loss] were summarized. Results Of the 267 AA patients, 61.4% were female; 53.2% were adults, 28.5% identified as Hispanic, 52.1% Non-Hispanic (NH)-White, 8.6% NH Black and 6.4% as NH Asian. 47.1% of NH Asian patients had severe disease (SALT>50) and represented the group with the highest proportion of SALT>50, followed by 34.8% of NH Black, 31.7% NH White, and 19.7% Hispanic AA patients. 47.1% of NH Asian patients reported PGIS-AA ‘severe/very severe’ disease, 41.9% of NH White, 39.1% of NH Black and 27.6% of Hispanic AA patients. Eyebrow involvement was highest in NH Whites (44.6%), followed by NH Asian (41.2%), NH Black (34.8%), and Hispanic AA patients (26.3%). Eyelash involvement was highest in NH Black patients, followed by NH White, NH Asian, and Hispanic patients (39.1%, 34.7%, 35.3%, and 17.1%, respectively). Discussion In this large real-world cohort, there are differences in clinician reported measures by race/ethnicity subgroups. NH-Asian patients represented the largest proportion of patients with patient and clinician-reported severe disease, and clinician-reported eyebrow / eyelash involvement was most prevalent in NH White patients. Hispanic patients had the smallest proportion with severe SALT, eyebrow and eyelash involvement. Additional research is required to better characterize AA and health-related quality of life burden in non-White AA patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".