Higher Risk of Thyroid Disorders in Young Patients with Type 1 Diabetes: A 12-Year Nationwide, Population-Based, Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: The association between type 1 diabetes and thyroid autoimmunity has been studied in various populations, but seldom on Taiwanese children and adolescents. Therefore, the aim of this study was to examine the incidence of autoimmune thyroid disorders in Taiwanese children and adolescent patients with type 1 diabetes, based on data from a nationwide, population-based, health claims database. METHODS: Using Taiwan's National Health Insurance Research Database, we identified 3,652 patients with type 1 diabetes between 2000 and 2012. A comparison cohort was assembled, which consisted of five patients without type 1 diabetes, based on frequency matching for sex and 3-year age interval, for each patient with type 1 diabetes. Both groups were followed until diagnosis of thyroid disorders or the end of the follow-up period. Poisson regression models were used to calculate incidence rate ratios for the thyroid disorders between the type 1 diabetes cohort and the comparison cohort. RESULTS: Simple and unspecified goiter (International Classification of Diseases, 9th Revision, Clinical Modification [ICD-9-CM] code 240), thyrotoxicosis (ICD-9-CM code 242), unspecified hypothyroidism (ICD-9-CM code 244.9), and thyroiditis (ICD-9-CM code 245) showed significantly higher incidences in the type 1 diabetes cohort compared with the control cohort, with incidence rate ratios of 2.74, 6.95, 6.54, 16.07, respectively. CONCLUSIONS: Findings from this nationwide, population-based cohort study showed that the incidences of autoimmune thyroid disorders were significantly higher in Taiwanese children and adolescents with type 1 diabetes compared with those without the disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".