Secondary Stroke Prevention in Ontario: A Population-Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Secondary stroke prevention can reduce subsequent vascular events, mortality and accumulation of disability. Current rates of adherence to secondary stroke prevention indicators are unknown. Our aim was to evaluate secondary stroke prevention care in Ontario, Canada. METHODS: A retrospective cohort study using health administrative databases included all adults discharged alive following an ischemic stroke from April 2010 to March 2019. Indicators of secondary stroke prevention, including laboratory testing, physician visits and receipt of routine influenza vaccinations, were evaluated among survivors in the one year following a stroke event. The use of medication was also assessed among individuals over the age of 65 years and within subgroups of stroke survivors with diabetes and atrial fibrillation. RESULTS: After exclusions, 54,712 individuals (mean age 68.4 years, 45.7% female) survived at least one year following their stroke event. In the 90 days following discharge from the hospital, most individuals (92.8%) were seen by a general practitioner, while 26.2% visited an emergency department. Within the year following discharge, 66.2% and 61.4% were tested for low-density lipoprotein and glycated hemoglobin, respectively, and 39.6% received an influenza vaccine. Among those over the age of 65 years, 85.5% were prescribed a lipid-lowering agent, and 88.7% were prescribed at least one antihypertensive medication. In those with diabetes, 70.3% were prescribed an antihyperglycemic medication, while 84.9% with atrial fibrillation were prescribed an anticoagulant. CONCLUSION: Secondary stroke prevention, especially for important laboratory values, remains suboptimal, despite thorough best practice guidelines. Future studies should explore barriers to better secondary stroke care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".