Quality Of Care And Mortality Following Stroke In Individuals With Schizophrenia
Bibliographic record
Abstract
Background and Aims:Individuals with schizophrenia appear to have a greater risk of death following stroke, compared to the general population. However, the reasons for this are not well understood. We used data from the province of Ontario, Canada, to compare stroke care and outcomes in people with and without schizophrenia.Methods:We used the Ontario Stroke Registry to identify patients hospitalized with stroke between April 1, 2002 and March 31, 2013, and identified those with schizophrenia using validated algorithms. We compared processes of acute stroke care delivery in those with and without schizophrenia and used Cox proportional hazards models to examine the association between schizophrenia and mortality, adjusting for demographics, stroke severity, and processes of care.Results:The study sample included 52,473 people, 612 (1.2%) of whom had schizophrenia. Individuals with compared to without schizophrenia were younger (median age 66 vs. 74 years) and more likely to arrive by ambulance (79.9% vs. 72.2%), but had longer median time to presentation (7.74 vs. 5.78 hours). The use of thrombolysis, stroke unit care, rehabilitation, and antiplatelet therapy was similar in those with and without schizophrenia; however, those with schizophrenia were less likely to undergo carotid imaging and to be treated with antihypertensive, lipid-lowering or anticoagulant medications. One year mortality was greater in those with compared to without schizophrenia (adjusted hazard ratio 1.32, 95% CI 1.14-1.54), even after adjustment for age, sex, and other factors.Conclusions:Schizophrenia is associated with increased mortality after stroke, despite similar processes of acute stroke care delivery.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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, 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".