Chronological association between alopecia areata and autoimmune thyroid diseases: A systematic review and meta‐analysis
Bibliographic record
Abstract
The association between alopecia areata (AA) and autoimmune thyroid diseases (AITD) has been suggested; however, the chronological relationship between AA and AITD remains elusive. A systematic review and meta-analysis were conducted to assess the association between AA and AITD focusing on the prevalence of thyroid antibodies, thyroid diseases and serological thyroid dysfunctions, respectively. Data collection was performed in October 2018 by searching for articles in two electronic databases: Medline and Embase. Case-control, cohort and cross-sectional studies were included. Meta-analysis of studies eligible for quantitative synthesis was performed to estimate pooled odds ratios of thyroid antibodies; thyroid peroxidase antibody (TPO-Ab) and thyroglobulin antibody (TG-Ab), diagnosed thyroid diseases and serological thyroid dysfunctions. Four hundred and eighty nine research papers were identified and 17 studies with 262 581 patients and 1 302 655 control subjects were included for quantitative synthesis. AA was significantly associated with both TPO-Ab and TG-Ab. In comparison, there was no significant association between AA and diagnosed hypothyroidism or hyperthyroidism and serological hypothyroidism or hyperthyroidism. In conclusion, AA is significantly associated with the existence of thyroid antibodies rather than with clinical or laboratory thyroid abnormality. Lack of long-term follow-up data is a limitation of the existing published work. Our findings do not support routine screening of thyroid diseases for asymptomatic AA patients but highlight the potential future risk of AITD particularly in severe and refractory AA.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.026 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".