Stroke Prophylaxis in Institutionalized Elderly Patients with Atrial Fibrillation
Bibliographic record
Abstract
OBJECTIVES: To identify patterns and predictors of antithrombotic use and to evaluate the appropriateness of antithrombotic therapy for stroke prophylaxis in institutionalized elderly patients with atrial fibrillation. DESIGN: Retrospective study. SETTING: Seventeen long-term care institutions in Edmonton, Alberta. SUBJECTS: Two hundred sixty-five long-term care residents, aged 65 and older, with atrial fibrillation. MEASUREMENTS: The proportion of patients who were prescribed warfarin, acetylsalicylic acid (ASA), both, or neither was determined. Odds ratios were calculated to identify risk factors for stroke and bleeding that are predictive of the receipt of anticoagulant therapy. Appropriateness of therapy was evaluated based on whether patients were prescribed antithrombotic therapy in accordance with their risk factors for stroke and bleeding. RESULTS: Warfarin was prescribed for 49% of patients, ASA for 22%, both for 8%, and neither for 20%. Nearly all patients (97%) were considered to be at high risk for stroke, with age being the predominant risk factor (88%>75), whereas about half were considered to be at low risk for bleeding. Multivariate analyses did not find any associations between individual risk factors for bleeding and anticoagulant treatment, with the exception of recent surgery (odds ratio=0.59, 95% confidence interval=0.37-0.94). Overall, 54.8% of patients received appropriate antithrombotic therapy. Of patients who were optimal candidates for anticoagulation, 60% received appropriate therapy (warfarin with or without ASA). CONCLUSION: Although warfarin was the most appropriate treatment in nearly all of this population at high risk for stroke, it was prescribed in fewer than two-thirds of patients. Antithrombotic therapy was not always prescribed in accordance with patients' risk factors for stroke and bleeding. There is a need for systematic identification of appropriate candidates for anticoagulation in the long-term care setting.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".