Review of global epidemiology data for alopecia areata highlights gaps and a call for action
Bibliographic record
Abstract
There is a clear need for improved and inclusive global prevalence and incidence data for alopecia areata (AA). New therapies, including Janus kinase inhibitors, are effective in some – but not all – patients. This suggests an incomplete understanding of disease pathogenesis, with possible contributions of alternate mechanisms of disease in specific patient subsets.1 Data from diverse geographical sites may uncover additional genetic polymorphisms, as well as external/environmental triggers for disease. These new data may identify additional novel targets for prevention or disease modification. Additionally, changes in incidence over time could inform clinicians and researchers about the possible contributions of regional, seasonal or infectious triggers such as airborne allergens or viruses like COVID-19.2 In this issue, Jeon et al. present a thorough systematic review of the population prevalence and incidence rates for AA, resulting in 80 data points from 28 countries.3 Overall, prevalence ranged from 0.02% to 0.21%. Higher prevalence rates were reported in adults than in children, but the authors were unable to present the data fully by subcategories such as race, ethnicity or other social determinants of health. There was a trend toward a higher prevalence of AA in high-income countries.
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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 | 0.027 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".