Abstract W P326: Evaluating Low Rates of Oral Anticoagulant Prescribing for Secondary Stroke Prevention in Ontario
Bibliographic record
Abstract
Background: Evidence for benefit of oral anticoagulation (OAC) therapy for stroke prevention in atrial fibrillation (AF) is well established, however, many observational studies have shown underuse and high rates of discontinuation of OAC in primary and secondary stroke prevention. The purpose of this study was to identify: 1) factors associated with low rates of OAC prescribing after stroke or TIA, and 2) factors associated with lack of adherence to warfarin within one year of stroke/TIA. Methods: Data from the Ontario Stroke Registry were used to identify a cohort of patients with AF and ischemic stroke or TIA admitted to 11 stroke centers in Ontario, Canada between 2003 and 2011. Patient demographic and clinical characteristics were compared in those prescribed and not prescribed OAC at hospital discharge and within one year of stroke/TIA. Warfarin adherence was determined using prescription claims data from the Ontario Drug Benefits database for patients over the age of 65. Multiple logistic regression was used to determine independent predictors of OAC prescribing at discharge and low warfarin adherence one year after stroke/TIA. Results: Of the 5781 patients identified, 4235 (73%) were prescribed OAC at hospital discharge. Overall, older patients were less likely to receive OAC at discharge (OR for each additional year 0.98, 95% CI 0.98 to 0.99), as were those with TIA compared to ischemic stroke (OR 0.70, 95% CI 0.60 to 0.82), prior gastrointestinal bleed (OR 0.47, 95% CI 0.36 to 0.62), renal disease (OR: 0.68, 95% CI 0.47 to 0.97), dementia (OR: 0.73, 95% CI 0.60 to 0.90), and those admitted from a longterm care facility (OR: 0.53, 95% CI 0.40 to 0.70). Patients with greater stroke severity (Canadian Neurological Score 4-8 compared to CNS >8) were more likely to receive OAC (OR 1.3, 95% CI 1.1 to 1.5). In contrast, at one year, patients with greater stroke severity (CNS <4 compared to CNS >8) were less likely to be adherent to warfarin therapy (OR 0.25, 95% CI 0.10 to 0.62). Conclusions: Age, dementia, and longterm care residence are predictors of OAC underuse in secondary stroke prevention and represent key areas to be targeted for quality improvement initiatives.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".