Atypical antipsychotics and hyperglycemic emergencies: Multicentre, retrospective cohort study of administrative data
Bibliographic record
Abstract
OBJECTIVE: To evaluate the relationship between initiation of atypical antipsychotic agents and the risk of hyperglycemic emergencies. METHOD: We conducted a multicentre retrospective cohort study using administrative health data from 7 Canadian provinces and the UK Clinical Practice Research Datalink. Hospitalizations for hyperglycemic emergencies (hyperglycemia, diabetic ketoacidosis, hyperosmolar hyperglycemic state) were compared between new users of risperidone (reference), and new users of olanzapine, other atypical antipsychotics, and typical antipsychotics. We used propensity scores with inverse probability of treatment weighting and proportional hazard models to estimate the site-specific hazard ratios of hyperglycemic emergencies in the year following drug initiation separately for adults under and over age 66 years. Site-level results were pooled using meta-analytic methods. RESULTS: Among 725,489 patients, 55% were aged 66+years; 5% of younger and 19% of older patients had pre-existing diabetes. Hyperglycemic emergencies were rare (1-2 per 1000 person years), but more frequent in patients with pre-existing diabetes (6-12 per 1000 person years). We did not find a significant difference in risk of hyperglycemic emergencies with initiation of olanzapine versus risperidone; however heterogeneity existed between sites. The risk of an event was significantly lower with other atypical (99% quetiapine) compared to risperidone use in older patients [adjusted hazard ratio, 95% confidence interval (CI): 0.69, 0.53-0.90]. CONCLUSIONS: Risk for hyperglycemic emergencies is low after initiation of antipsychotics, but patients with pre-existing diabetes may be at greater risk. The risk appeared lower with the use of quetiapine in older patients, but the clinical significance of the findings requires further study.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".