Exploring the risk of diabetes mellitus and dyslipidemia among ambulatory users of atypical antipsychotics: a population‐based comparison of risperidone and olanzapine
Bibliographic record
Abstract
PURPOSE: To compare the incidence rates of diabetes mellitus and dyslipidemia in ambulatory first-time users of risperidone and olanzapine. METHODS: The database for the Prescription Drug Insurance Plan in the province of Quebec was used as the data source for a population-based cohort study. Denominalized data were extracted for all ambulatory patients who first received an atypical antipsychotic between 1 January 1997 and 31 August 1999. Eligible patients were categorized as taking: no antidiabetic medication; no lipid reducing medication; neither type of medication. Those who started to use an outcome drug (an antidiabetic or lipid-lowering medication) before the end of the follow-up period (31 August 2000) were considered to have developed the corresponding outcome disease. Incidence rate ratios (IRR) (and 95% confidence intervals) for initiating antihyperglycemic or lipid-lowering drug treatment, or both were calculated. Outcomes on risperidone were compared to those on olanzapine. RESULTS: A total of 19 582 eligible patients were included in the analysis. Relative to risperidone, olanzapine was associated with a higher risk of initiating a pharmacologic treatment for diabetes [IRR: 1.33 (1.03-1.74)], dyslipidemia [IRR: 1.49 (1.22-1.83)], or either condition [1.47 (1.23-1.76)]. CONCLUSIONS: Olanzapine seems to be associated with a higher risk of developing diabetes and/or dyslipidemia than risperidone. Further prospective studies are needed to rigorously assess the safety of olanzapine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".