Atrial fibrillation and stroke: how much atrial fibrillation is enough to cause a stroke?
Bibliographic record
Abstract
PURPOSE OF REVIEW: The association between atrial fibrillation and stroke is firmly established, and anticoagulation reduces stroke risk in patients with atrial fibrillation. However, the role of anticoagulation in very brief durations of atrial fibrillation (subclinical atrial fibrillation) is an area of controversy. RECENT FINDINGS: Stroke risk increases alongside burden of atrial fibrillation. Ongoing trials will clarify if 24 h or less of atrial fibrillation on extended monitoring necessitates lifelong anticoagulation. Trials examining empiric anticoagulation for individuals with ESUS did not demonstrate benefit over antiplatelet agents. However, hypothesis-generating sub-analyses suggest that certain at-risk groups may benefit. Atrial cardiopathy is associated with subclinical atrial fibrillation and research examining anticoagulation after ESUS in this population is underway. SUMMARY: Stroke risk increases alongside burden of ectopic atrial activity. However, this risk may in part be because of prothrombotic dysfunction associated with atrial cardiopathy in addition to the arrhythmia itself. The minimal amount of subclinical atrial fibrillation to warrant anticoagulation for stroke prevention, and how this may be modified by the total duration of monitoring, will be clarified by the results of ongoing clinical trials. Currently research will also help identify whether a select group of ESUS patients who have structural and electrophysiological markers of atrial cardiopathy warrant anticoagulation for secondary prevention.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".