Prevalence of Celiac Disease in Patients with Autoimmune Thyroid Disease: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Several screening studies have indicated an increased prevalence of celiac disease (CD) among individuals with autoimmune thyroid disease (ATD), but estimates have varied substantially. OBJECTIVE: The aim of this study was to examine the prevalence of CD in patients with ATD. METHOD: A systematic review was conducted of articles published in PubMed Medline or EMBASE until September 2015. Non-English papers with English-language abstracts were also included, as were research abstracts without full text available when relevant data were included in the abstract. Search terms included "celiac disease" combined with "hypothyroidism" or "hyperthyroidism" or "thyroid disease." Fixed-effects inverse variance-weighted models were used. Meta-regression was used to examine heterogeneity in subgroups. RESULTS: A pooled analysis, based on 6024 ATD patients, found a prevalence of biopsy-confirmed CD of 1.6% [confidence interval (CI) 1.3-1.9%]. Heterogeneity was large (I(2) = 70.7%). The prevalence was higher in children with ATD (6.2% [CI 4.0-8.4%]) than it was in adults (2.7%) or in studies examining both adults and children (1.0%). CD was also more prevalent in hyperthyroidism (2.6% [CI 0.7-4.4%]) than it was in hypothyroidism (1.4% [CI 1.0-1.9%]). CONCLUSIONS: About 1/62 patients with ATD have biopsy-verified CD. It is argued that patients with ATD should be screened for CD, given this increased prevalence.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.072 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".