Comparative Safety of Antipsychotics: Another Look at the Risk of Diabetes
Bibliographic record
Abstract
OBJECTIVE: The association between the use of antipsychotics and diabetes mellitus (DM) is still unclear, as depicted by several conflicting reports. Our study aims to assess the risk of DM in new users of antipsychotics. METHODS: Our nested case-control study used the Quebec Health Insurance Board databases. People in the source cohort were DM-free and had initiated an antipsychotic treatment. Subjects were cohort members who initiated an antidiabetic or had a diagnosis of DM during their follow-up period. Three variables were used to assess antipsychotic exposure: the antipsychotic used (any typical, clozapine, olanzapine, quetiapine, risperidone, or more than 1 drug); the number of 30-day periods of use; and antipsychotic use at index date (current or past). A paired multivariate logistic regression model was used to calculate adjusted odds ratios. RESULTS: Among the 88 467 people included in the cohort, 6109 subjects with DM were identified and were matched to 61 090 control subjects. New users of quetiapine were less likely to develop DM than new users of typical antipsychotics (OR, 0.89; 95% CI 0.81 to 0.99). The risk of DM was not statistically different across the atypical antipsychotics. A longer exposure to any antipsychotic (for each 30-day period, OR 1.009; 95% CI 1.006 to 1.011) and current use of antipsychotics (OR 1.26; 95% CI 1.17 to 1.36) were associated with DM. CONCLUSION: These results suggest that metabolic parameters of people exposed to antipsychotics should be monitored, irrespective of the drug taken, among the drugs available at the time of analysis.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".