Ambulatory Use of Olanzapine and Risperidone: A Population-Based Study on Persistence and the Use of Concomitant Therapy in the Treatment of Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: To assess treatment discontinuation and concomitant use of other antipsychotics among individuals initiated on olanzapine or risperidone for the treatment of schizophrenia. METHOD: Using data from the Quebec health insurance plan and the Quebec database for hospitalization, we conducted a population-based cohort study of patients for whom a first claim for olanzapine or risperidone was submitted between 1 January 1997 and 31 August 1999. Included were 6405 patients with schizophrenia whom we followed from the date of the first claim for olanzapine or risperidone either to discontinuation date, end of eligibility for the drug plan, 365 days, date of moving out of the province, or date of death. We used Cox regression models to compute hazards ratios (HRs) of having the treatment discontinued and logistic regression models to compute odds ratios (ORs) among persisting patients of having any concomitant antipsychotic prescription. All models were adjusted for age, sex, schizophrenia disorder, comorbidity, region, beneficiary type, substance use disorder, and prior hospitalization for mental illness. RESULTS: Compared with risperidone users (n = 2718), discontinuation rates were lower for olanzapine users (n = 3687; HR = 0.79; 95%CI, 0.74 to 0.84). The odds of receiving any concomitant antipsychotic prescription did not differ statistically between olanzapine and risperidone users (OR 0.85; 95%CI, 0.71 to 1.01). CONCLUSIONS: The study results suggest that new users of olanzapine were less likely to discontinue their initial treatment than were new users of risperidone, although discontinuation was high in both groups. Among those who persisted, concomitant use of other antipsychotics did not differ between olanzapine users and risperidone users.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".