The Risk of Diabetes During Olanzapine Use Compared With Risperidone Use
Bibliographic record
Abstract
BACKGROUND: The relative risk of diabetes among patients undergoing risperidone treatment was compared with that of patients receiving olanzapine. METHOD: A cohort was formed of 33,946 patients with at least 1 prescription for either olanzapine (N = 19,153) or risperidone (N = 14,793) between January 1, 1997, and December 31, 1999, recorded in the Régie de l'Assurance Maladie du Québec database. Patients were excluded if clozapine was dispensed to them during the study period or if they were diagnosed with diabetes before beginning antipsychotic therapy. New diabetes diagnoses made after the first antipsychotic prescription during the period were tabulated until December 31, 1999; censoring occurred at this date or at the last service date, if there was no record of using services during the last 6 months of follow-up. Crude hazard ratio and proportional hazard analyses were carried out. RESULTS: 319 patients developed diabetes on olanzapine treatment, and 217 developed diabetes on risperidone treatment; a crude hazard ratio of 1.08 (95% CI = 0.89 to 1.31, p =.43) was determined. When age, gender, and haloperidol use were controlled for using proportional hazard analysis, there was a 20% increased risk of diabetes with olanzapine relative to risperidone (95% CI = 0% to 43%, p =.05). Proportional hazard analyses adjusted for duration of olanzapine exposure indicated that the first 3 months of olanzapine treatment was associated with an increased risk of diabetes of 90% (95% CI = 40% to 157%, p <.0001), after adjusting for age, gender, and haloperidol use. CONCLUSION: Compared with risperidone, olanzapine was associated with an increased risk of developing diabetes. More studies are required to further investigate this association.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".