Ischemic stroke prevention in patients with atrial fibrillation and a recent ischemic stroke, TIA, or intracranial hemorrhage: A World Stroke Organization (WSO) scientific statement
Bibliographic record
Abstract
BACKGROUND: Secondary stroke prevention in patients with atrial fibrillation (AF) is one of the fastest growing areas in the field of cerebrovascular diseases. This scientific statement from the World Stroke Organization Brain & Heart Task Force provides a critical analysis of the strength of current evidence on this topic, highlights areas of current controversy, identifies knowledge gaps, and proposes priorities for future research. METHODS: We select topics with the highest clinical relevance and perform a systematic search to answer specific practical questions. Based on the strength of available evidence and knowledge gaps, we identify topics that need to be prioritized in future research. For this purpose, we adopt a novel classification of evidence strength based on the availability of publications in which the primary population is patients with recent (<6 months) cerebrovascular events, the primary study endpoint is a recurrent ischemic stroke, and the quality of the studies (e.g. observational versus randomized controlled trial). SUMMARY: Priority areas include AF screening, molecular biomarkers, AF subtype classification, anticoagulation in device-detected AF, timing of anticoagulation initiation, effective management of breakthrough strokes on existing anticoagulant therapy, the role of left atrial appendage closure, novel approaches, and antithrombotic therapy post-intracranial hemorrhage. Strength of currently available evidence varies across the selected topics, with early anticoagulation being the one showing more consistent data. CONCLUSION: Several knowledge gaps persist in most areas related to secondary stroke prevention in AF. Prioritizing research in this field is crucial to advance current knowledge and improve clinical care.
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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 | 0.112 | 0.181 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".