Abstract WMP54: Stroke Secondary Prevention Care In Persons With Schizophrenia
Bibliographic record
Abstract
Introduction: Schizophrenia is associated with stroke incidence and case-fatality but the causes of this are not well-understood. We evaluated the association between comorbid schizophrenia and the quality of secondary prevention care after ischemic stroke. Methods: We used linked health administrative data to identify adults discharged alive from acute care hospitals in Ontario, Canada between 2004 and 2017 following an incident ischemic stroke and identified those with a history of schizophrenia using a validated algorithm. Outcomes were screening, treatment among those with the risk factor, control of vascular risk factors, and receipt of outpatient physician services. We used modified Poisson regression to model the relative risk of each indicator outcome among persons with and without schizophrenia who were alive one year after discharge, adjusting for age, sex, income, comorbidity and rural residence. Results: Among 85,046 persons with ischemic stroke, 886 (1.04%) had a diagnosis of schizophrenia. Those with schizophrenia were younger (median age 64 vs 72), more likely to be female (50.1% vs 44.6%), live in the lowest neighbourhood income quintile (41.1% vs 23.0%) and have diabetes (42.8% vs 32.3%). Of those alive 1 year after stroke, those with schizophrenia were less likely to be screened for hyperlipidemia (adjusted relative risk (aRR) 0.89; 95% confidence interval (CI) 0.84 to 0.94) or diabetes (aRR 0.93; 95% CI 0.89 to 0.97), be prescribed antihypertensive agents (aRR 0.96; 95% CI 0.93 to 0.99), or achieve target lipid levels (LDL < 2 mml/L) (aRR 0.87; 95% CI 0.78 to 0.96). There were no differences in prescription of antilipemic (aRR 0.96; 95% CI 0.91 to 1.01) or antiglycemic (aRR 0.95; 95% CI 0.87 to 1.03) agents or in achievement of target HbA1c ≤ 7% (aRR 0.89; 95% CI 0.78 to 1.02). Outpatient stroke specialist care in the 3 months after discharge was less likely among those with schizophrenia (aRR 0.72; 95% CI 0.65 to 0.78) as were visits to primary care physicians (aRR 0.93; 95% CI 0.91 to 0.96). Conclusions: People with schizophrenia and stroke are less likely to receive secondary preventive care across many indicators measured. This information may be useful for targeted interventions to improve post-stroke care 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".