Secondary Stroke Prevention in People With Schizophrenia
Bibliographic record
Abstract
BACKGROUND: People with schizophrenia are less likely than those without to be treated for cardiovascular disease. We aimed to evaluate the association between schizophrenia and secondary preventive care after ischemic stroke. METHODS AND RESULTS: In this retrospective cohort study, we used linked population-based administrative data to identify adults who survived 1 year after ischemic stroke hospitalization in Ontario, Canada between 2004 and 2017. Outcomes were screening, treatment, and control of risk factors, and receipt of outpatient physician services. We used modified Poisson regression to model the relative risk of each outcome among people with and without schizophrenia, adjusting for age and other factors. Among 81 163 people with ischemic stroke, 844 (1.04%) had schizophrenia. Schizophrenia was associated with lower rates of screening for hyperlipidemia (60.5% versus 66.0%, adjusted relative risk [aRR] 0.88 [95% CI, 0.84-0.93]) and diabetes (69.4% versus 73.9%, aRR 0.93 [95% CI, 0.89-0.97]), prescription of antihypertensive medications (91.2% versus 94.7%, aRR 0.96 [95% CI, 0.93-0.99]), achievement of target lipid levels (low-density lipoprotein <2 mmol/L) (30.6% versus 34.6%, aRR 0.86 [95% CI, 0.78-0.96]), and outpatient specialist visits (55.3% versus 67.8%, aRR 0.78 [95% CI, 0.74-0.83]) or primary care physician visits (94.5% versus 98.5%; aRR 0.96 [95% CI, 0.95-0.98]) within 1 year. There were no differences in prescription of antilipemic, antiglycemic, or anticoagulant medications, or in achievement of target hemoglobin A1c ≤7%. CONCLUSIONS: People with stroke and schizophrenia are less likely than those without to receive secondary preventive care. This may inform interventions to improve poststroke care and outcomes in those with schizophrenia.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".