Comparative effectiveness and safety of antipsychotic drugs in schizophrenia treatment: a real‐world observational study
Bibliographic record
Abstract
OBJECTIVE: The objective was to compare, in a real-world setting, the risk of mental and physical health events associated with different antipsychotic drugs (clozapine, olanzapine, risperidone, quetiapine and first-generation antipsychotics) in patients with SZ. METHODS: This is a retrospective cohort study using administrative data. Outcome measures included any mental health event (suicide, hospitalization or emergency visit for mental disorders) and physical health event (death other than suicide, hospitalization or emergency visit for physical disorders). Cox proportional hazard models were used to estimate the hazard ratios of the events associated with the use of the different antipsychotic drugs. RESULTS: The cohort included 18 869 adult patients living in the province of Quebec (Canada) with SZ and starting antipsychotic drugs between January 1998 and December 2005. Results show that quetiapine and not using any antipsychotics were associated with an increased risk of mental and physical health events as compared to other drugs. The second finding is the confirmation of better performance of clozapine. The results were robust across sensitivity analyses. CONCLUSION: Both findings call for an international public health and drug agencies surveillance of 'real-world' antipsychotic medication to ensure the optimal choices in treatment guidelines for SZ.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".