Management and Genetics of Alopecia Areata within the USA: A Cross-Sectional Study of All of Us
Bibliographic record
Abstract
Introduction: Alopecia areata (AA) is a difficult to treat and appearance altering disorder affecting up to 2% of people during their lifetime. Understanding current management trends will help in improving patient outcomes. The aim of this study was to determine the impact of comorbid disorders and demographic factors on the management of AA and determine the influence of previously discovered genetic factors in different ethnic groups. Methods: We used the All of Us controlled dataset (version 7) and examined electronic health record and genomic data from 206,173 participants in a retrospective cross-sectional study conducted in outpatients in the USA. Results: We found that AA patients with comorbid atopic dermatitis, psoriasis, and vitiligo were more likely to have been prescribed topical corticosteroids. Patients that were not of European/Caucasian ancestry were less likely to be prescribed any type of corticosteroid. We also found that specific genetic variations (single nucleotide polymorphisms) that increased or decreased risk in European/Caucasian participants did not necessarily have the same effect in other ethnicities (Hispanics and blacks). Conclusion: This work has helped uncover the state of AA care within the USA and has identified access to healthcare inequities in different ethnic populations.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".