Cerebro‐ and cardiovascular conditions in adults with schizophrenia treated with antipsychotic medications
Bibliographic record
Abstract
OBJECTIVE: To report on the relative risk of cerebro- and cardiovascular disorders associated with antipsychotic treatment among adults with schizophrenia. METHOD: Medical and pharmacy claims data from the South Carolina Medicaid program were extracted to compare the prevalence rates for four coded cerebrovascular (cerebrovascular disease; cerebrovascular accident; cerebrovascular hemorrhage; and peripheral vascular disease) and four cardiovascular (myocardial infarction; ischemic heart disease; arrhythmias; and cardiomyopathy) conditions. The analysis employed a retrospective cohort design with a 3 years time period as the interval of interest. Schizophrenic adults (18-54) (n = 2251) prescribed one of six atypical or two conventional antipsychotic medications were identified and comprised the analysis set. RESULTS: Incidence rates for cerebrovascular disorders ranged from 0.5 to 3.6%. No significant association between antipsychotic usage and cerebrovascular disorders was noted largely due to the low base rate. Incidence rates for overall cardiovascular conditions ranged from 6 to 20%. The odds of developing cardiomyopathy were significantly lower for aripiprazole (OR = -3.45; p = 0.02), while the odds of developing hypertension were significantly lower for males (OR = -1.37; p = 0.009) but significantly higher for patients prescribed ziprasidone (OR = 1.91; p = 0.01) relative to conventional antipsychotics. CONCLUSION: No significant association between antipsychotic usage and cerebro- or cardiovascular disorders was noted.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".