Higher Incidence of Psychiatrist-Diagnosed Depression in Taiwanese Female School-Age Children and Adolescents with Type 1 Diabetes: A Nationwide, Population-Based, Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to investigate the risk of clinical depression associated with type 1 diabetes in female Taiwanese children and adolescents. METHODS: Using Taiwan's National Health Insurance Research Database, we identified 1373 female children and adolescents, aged 5-18 years, with type 1 diabetes diagnosed between 2000 and 2007. A comparison cohort was assembled, which consisted of 20 patients without type 1 diabetes, based on frequency matching for age interval and index year for each patient with type 1 diabetes. Both groups were followed until a psychiatrist diagnosed depression or the end of the follow-up period, up to a maximum period of 5 years from the index date. A Poisson regression model was used to calculate incidence rate ratios (IRRs) for depression between the type 1 diabetes cohort and the comparison cohort. RESULTS: The incidence rate of depression in the type 1 diabetes cohort was 228.4 per 100,000 person-years and that in the comparison cohort was 73.9 per 100,000 person-years. The type 1 diabetes cohort showed a significantly higher incidence of depression compared with the comparison cohort (IRR of 3.09, p < 0.001). CONCLUSION: Findings from this nationwide, population-based, retrospective cohort study showed that the incidence of psychiatrist-diagnosed depression was significantly higher in female Taiwanese school-age children and adolescents with type 1 diabetes compared with those without the disease.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".