Stroke prevention for patients with atrial fibrillation: improving but not perfect yet
Bibliographic record
Abstract
Atrial fibrillation (AF) is the most common cardiac rhythm disorder1 and one of the most important risk factors for stroke, particularly in the elderly. Stroke-related AF is associated with significant morbidity, mortality and healthcare costs.2 Although we have abundant evidence from randomised trials that anticoagulation, and to a lesser extent antiplatelet therapy, is highly efficacious in preventing stroke in patients with AF, these therapies remain underused, especially in older patients. With an ageing population and an AF prevalence that is rapidly rising,1 a better understanding of the stroke prevention practices in real-world settings is critically important to implement preventive strategies that will improve the outcomes and reduce healthcare costs. Cowan et al 3 report the results of their investigation on the use of stroke prevention therapy for management of AF among 1857 primary-care practices across England from July 2009 to March 2012. Using the Guidance on Risk Assessment and Stroke Prevention in AF (GRASP-AF) tool, they identified 231 833 patients with AF (1.76% of the total population studied) and characterised their risk profiles and antithrombotic therapies. The GRASP-AF tool is valuable for gathering patient data at the primary-care level, and this is a particular …
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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".