Adolescent Thyroid Disorders and Risk for Type 2 Diabetes in Young Adulthood
Bibliographic record
Abstract
CONTEXT: Thyroid hormones play a key role in systemic metabolism, yet the relationship between thyroid dysfunction and risk for type 2 diabetes is unclear. OBJECTIVE: To assess type 2 diabetes risk in adulthood among adolescents with thyroid disorders. DESIGN AND SETTING: A nationwide, population-based study of Israeli adolescents who were examined before military recruitment during 1988 to 2007 and were followed until December 31, 2016. PARTICIPANTS: 1 382 560 adolescents (mean age 17.3 years). INTERVENTIONS: The diagnosis of thyroid disorders was based on recent thyroid function tests. Data were linked to the Israeli National Diabetes Registry. Cox proportional hazard models were applied. MAIN OUTCOME MEASURES: Type 2 diabetes incidence. RESULTS: During a mean follow-up of 18.5 years, 1.12% (69 of 6,152) of adolescents with thyroid disorders were diagnosed with type 2 diabetes vs 0.77% of adolescents without thyroid disorders. The hazard ratio (HR) for type 2 diabetes was 2.3 (95% CI, 1.8-2.9) among those with thyroid disorders, after adjustment for sex, birth-year, body mass index, and sociodemographic confounders. The increased diabetes risk was observed in both men and women, with the presence or absence of obesity, and in the absence of other health conditions and was associated with different types of thyroid disorders. It was also similar when the outcome was defined as type 2 diabetes diagnosed at or before the age of 30 years (HR 2.3, 95% CI, 1.5-3.5). CONCLUSIONS: Thyroid disorders diagnosed in adolescence are a risk factor for early-onset type 2 diabetes in both men and women.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".