Antipsychotics and Risks of Cardiovascular and Cerebrovascular Diseases and Mortality in Dwelling Community Older Adults
Bibliographic record
Abstract
This study aims to investigate the effect of antipsychotic agents on cardiovascular and cerebrovascular diseases (CVD/CEV) and mortality risks in the older population living in a community. A cohort of 42,650 new users of antipsychotic agents was built using Quebec healthcare databases (1998-2011). The outcomes were CVD/CEV and mortality incidence in 5 years of follow-up in the total cohort, sub-cohort of patients with no schizophrenia/dementia, sub-cohort with schizophrenia, and sub-cohort with dementia. Comparisons were made between the new users who continued the treatment (adherent level ≥ 60%) vs. those ceasing treatment (adherence level < 60%) using inverse probability of treatment weighting and Cox models. Comparing high adherence vs. low levels, CVD/CEV risk was increased by 36% in the sub-cohort with schizophrenia for atypical antipsychotic users and by 25% in the sub-cohort with dementia for typical antipsychotic users. An increasing mortality risk of 2- to 3-fold was linked with the typical antipsychotic use in all cohorts except the sub-cohort with schizophrenia; in addition, mortality risk is linked with the use of high vs. low doses. Antipsychotics were not linked with CVD/CEV risk, except for atypical antipsychotics in patients with schizophrenia and typical antipsychotics in patients with dementia. The mortality risk was linked with the use of typical antipsychotics and the dose used.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".