A Systematic Approach to Stroke Prevention for Patients with Atrial Fibrillation
Bibliographic record
Abstract
Strokes occurring as a result of atrial fibrillation are common and typically result in severe disability or death. Over the past half century, therapeutic options for stroke prophylaxis, based on either antiplatelet (typically acetylsalicylic acid) or warfarin therapy, have remained virtually unchanged. However, as of mid-2011, promising data have emerged and Health Canada has approved a novel oral anticoagulant, dabigatran. This article provides a systematic 4-step process to guide clinicians in assessing, implementing and monitoring stroke prophylaxis for individual patients. First, identify the patient's risk of stroke with user-friendly scoring systems (CHADS 2 and CHA 2 DS 2 -VASc). Second, determine the patient's risk of major bleeding with a validated scoring system (HAS-BLED) and ongoing clinical evaluation. Third, balance these benefits and the risks of available agents as they pertain to the individual patient. Fourth, select the appropriate antithrombotic therapy, with an understanding of the key features of available agents, as well as the patient's characteristics and preferences. Regular monitoring and patient adherence with therapy are necessary to ensure the long-term appropriateness of therapy, given that most patients with atrial fibrillation will require lifelong stroke prophylaxis and an individual's stroke risk generally increases with age. The pharmacist is in an excellent position to provide this type of assessment and follow-up.
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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 | 0.030 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".