Incidence of Diabetes in Patients with Schizophrenia: A Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: To detect the incidence of diabetes in patients with schizophrenia in Taiwan. METHODS: The National Health Research Institute provided a database of 1 million random subjects for study, from which we drew a random sample of 617 068 subjects aged 18 years and older in the year 2000. Subjects who had at least one service claim during this year, with a primary diagnosis of schizophrenia, diabetes, or with a prescription for treatment of diabetes, were identified. We compared initial diagnosis of diabetes between patients with schizophrenia and the general population in 2000. We also followed a cohort of subjects with schizophrenia from 2000 to 2005. RESULTS: The incidence of diabetes was higher in patients with schizophrenia than in the general population (1.46% and 1.12%, respectively; OR 1.47; 95% CI 1.09 to 1.97) in 2000. Compared with the general population, patients with schizophrenia showed a higher incidence of diabetes in the group aged 18 to 29 years; among females; among those with insurance of more than US$1281; among those living in the northern region; and among those residing in urban areas. The average annual incidence of diabetes in patients with schizophrenia was 1.84% from 2000 to 2005. Higher incidence of diabetes in patients with schizophrenia was associated with increased age, females, hypertension, and hyperlipidemia. CONCLUSIONS: Patients with schizophrenia had a higher incidence of diabetes for the youngest adult age group and for females than for the general population. Increased age, females, hypertension, and hyperlipidemia were risk factors of diabetes in patients with schizophrenia.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".