Risk of Acute Complications of Diabetes Among People With Schizophrenia in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: Diabetes mellitus (DM) is a complex, chronic disease requiring active self-management and coordinated care. This study aimed to evaluate the relationship between schizophrenia and risk of preventable, acute DM complications. RESEARCH DESIGN AND METHODS: With the use of administrative data, a retrospective study assessed acute DM complications (emergency department [ED] visits or hospitalization for hypo- or hyperglycemia and hospital admissions for infections) among Ontario residents ages 18-50 with schizophrenia and newly diagnosed DM between 1995 and 2005, comparing people with and without pre-existing schizophrenia. Primary outcome was ED visit or hospitalization for hypo- or hyperglycemia. Secondary outcome was the first of either the primary outcome or hospitalization for infection. RESULTS: People with schizophrenia had a 74% greater risk of requiring a hospital visit for hypo- or hyperglycemia (hazard ratio [HR] = 1.74, 95% confidence interval 1.42-2.12) compared with those without schizophrenia. The risk was similar when the outcome included infection (HR = 1.62, 95% CI 1.39-1.89). Outcomes remained significant after adjustment for baseline characteristics. CONCLUSIONS: People with schizophrenia are at greater risk for developing an acute complication of DM. Understanding this relationship will direct future studies assessing barriers to care and implementation of individualized approaches to care for this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".