Low Use of Oral Anticoagulant Prescribing for Secondary Stroke Prevention
Bibliographic record
Abstract
BACKGROUND: Oral anticoagulation reduces the risk of stroke in atrial fibrillation but is often underused. OBJECTIVES: To identify factors associated with oral anticoagulant prescribing and adherence after stroke or transient ischemic attack (TIA). RESEARCH DESIGN: Retrospective cohort study using linked Ontario Stroke Registry and prescription claims data. SUBJECTS: Consecutive patients with atrial fibrillation and ischemic stroke/TIA admitted to 11 stroke centers in Ontario, Canada between 2003 and 2011. MEASURES: We used modified Poisson regression models to determine predictors of anticoagulant prescribing and multiple logistic regression to determine predictors of 1-year adherence. RESULTS: Of the 5781 patients in the study cohort, 4235 (73%) were prescribed oral anticoagulants at discharge. Older patients were less likely to receive anticoagulation [adjusted relative risk (aRR) for each additional year=0.997; 95% confidence interval (CI), 0.995-0.998], as were those with TIA compared with ischemic stroke (aRR=0.904; 95% CI, 0.865-0.945), prior gastrointestinal bleed (aRR=0.778; 95% CI, 0.693-0.873), dementia (aRR=0.912; 95% CI, 0.856-0.973), and those from a long-term care facility (aRR=0.810; 95% CI, 0.737-0.891). After limiting the sample to those without obvious contraindications to anticoagulation, age, dementia, and long-term care residence continued to be associated with lower prescription of oral anticoagulants. One-year adherence to therapy was similar across most patient groups. CONCLUSIONS: Age, dementia, and long-term care residence are predictors of lower oral anticoagulant use for secondary stroke prevention and represent key target areas for quality improvement initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".