Association of atopic dermatitis with thyroid diseases: A systematic review and meta‐analysis
Bibliographic record
Abstract
Atopic dermatitis (AD) and thyroid diseases share features of immune dysregulation, but their association has not yet been comprehensively analyzed. We aimed to systematically investigate the association of AD with thyroid diseases. A systematic review and random-effects model meta-analysis of observational studies was conducted. We searched MEDLINE, Embase, and CENTRAL for relevant studies until April 12, 2024. The risk of bias was assessed using the Newcastle-Ottawa Scale. A subgroup analysis based on age was performed. Twelve observational studies with 93,547,813 subjects were included. The meta-analysis of ten case-control studies revealed significant association of AD with prevalent thyroid diseases (odds ratio [OR]1.48; 95% confidence interval [CI] 1.17-1.88), including Hashimoto disease (OR 2.13; 95% CI 1.33-3.43) and Graves' disease (OR 1.56; 95% CI 1.03-2.37). Pediatric patients exhibited a stronger association (OR 1.88; 95% CI 1.48-2.37) than adults (OR 1.34; 95% CI 1.00-1.80). Two cohort studies demonstrated that AD patients had increased risks of incident thyroid diseases (incidence risk ratio 1.13; 95% CI 1.05-1.22) and Hashimoto disease (hazard ratio 1.17; 95% CI 1.09-1.25). In conclusion, AD is associated with thyroid diseases, notably in pediatric patients, warranting endocrinological consultation and early intervention to mitigate potential impacts on growth and cognitive development.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".