Bilateral Association between Atopic Dermatitis® and Alopecia Areata: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract: This systematic review and meta-analysis aimed to explore the association between atopic Dermatitis® (AD) and alopecia areata (AA). A comprehensive search was conducted in PubMed, Embase, Cochrane, and Web of Science from the inception of each database to November 10, 2022 for relevant studies. As there is a potential bilateral association between the 2 diseases, we assessed the prevalence/incidence of AA in patients with AD and the prevalence/incidence of AD in patients with AA. A total of 29 studies involving 11,233,448 participants were included in this analysis. AA was the exposure factor in 23 studies, AD in 7 studies, and both in 1 study. The meta-analysis revealed that the prevalence of AD was 11.2% (7.7%–15.1%) in patients with AA, and the prevalence of AA was 3.2% (95% confidence interval [CI]: 0.0%–11.5%) in patients with AD. The incidence of AD in AA patients was found to vary with age ( P = 0.07). Based on 7 studies, there was a significant association between AD and AA when AA was the exposure factor [odds ratio, OR, = 4.537 (95% CI: 2.409–8.544)]; based on 10 studies, there was also a significant association between AD and AA when AD was the exposure factor [OR = 2.643 (95% CI: 1.737–3.995)]. In conclusion, this meta-analysis demonstrated the 2-way association between AD and AA, providing a clinical reference for disease prevention and control.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".