Stroke care and case fatality in people with and without schizophrenia: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Schizophrenia is associated with an increased risk of death following stroke; however, the magnitude and underlying reasons for this are not well understood. OBJECTIVE: To determine the association between schizophrenia and stroke case fatality, adjusting for baseline characteristics, stroke severity and processes of care. DESIGN: Retrospective cohort study used linked clinical and administrative databases. SETTING: All acute care institutions (N=152) in the province of Ontario, Canada. PARTICIPANTS: All patients (N=52 473) hospitalised with stroke between 1 April 2002 and 31 March 2013 and included in the Ontario Stroke Registry. Those with schizophrenia (n=612) were identified using validated algorithms. MAIN OUTCOMES AND MEASURES: We compared acute stroke care in those with and without schizophrenia and used Cox proportional hazards models to examine the association between schizophrenia and mortality, adjusting for demographics, comorbidity, stroke severity and processes of care. RESULTS: Compared with those without schizophrenia, people with schizophrenia were less likely to undergo thrombolysis (10.1% vs 13.4%), carotid imaging (66.3% vs 74.0%), rehabilitation (36.6% vs 46.6% among those with disability at discharge) or be treated with antihypertensive, lipid-lowering or anticoagulant therapies. After adjustment for age and other factors, schizophrenia was associated with death from any cause at 1 year (adjusted HR (aHR) 1.33, 95% CI 1.14 to 1.54). This was mainly attributable to early deaths from stroke (aHR 1.47, 95% CI 1.20 to 1.80, with survival curves separating in the first 30 days), and the survival disadvantage was particularly marked in those aged over 70 years (1-year mortality 46.9% vs 35.0%). CONCLUSIONS: Schizophrenia is associated with increased stroke case fatality, which is not fully explained by stroke severity, measurable comorbid conditions or processes of care. Future work should focus on understanding this mortality gap and on improving acute stroke and secondary preventive care in people with schizophrenia.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".