Stroke Prevention for Patients with Atrial Fibrillation: Beyond the Guidelines
Bibliographic record
Abstract
Atrial fibrillation (AF) is the most common serious heart rhythm disorder, with a lifetime incidence of 1 in 4 for patients >40 years of age[1]. AF is a major cause of death and disability, as it is associated with a 4-5 fold increase in the risk of ischemic stroke[2]. In patients with AF, oral anticoagulation (OAC) therapy can reduce the risk of stroke by about two-thirds and the risk of all-cause mortality by approximately one-quarter, but is associated with an increased risk of bleeding[3], [4]. Atrial fibrillation (AF) is the most common serious heart rhythm disorder and is associated with an increased risk of ischemic stroke. This risk can be moderated with oral anticoagulation therapy, but the decision to do so must be balanced against the risks of bleeding. Herein, we discuss three emerging areas where more high-quality evidence is required to guide risk stratification: 1) the relationships between the pattern and burden of AF and stroke 2) the risk conferred by short episodes of device-detected "sub-clinical" atrial fibrillation (SCAF) and 3) the significance of AF that occurs transiently with stress (AFOTS), as is often detected during medical illness or after surgery. Risk stratification is important to identify patients with AF who can benefit from OAC therapy. There are, however, several common clinical scenarios where guidelines do not yet provide direction for stroke prevention; or do so based on limited high-quality evidence.
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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 | 0.011 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".