Ischaemic stroke in anticoagulated patients with atrial fibrillation
Bibliographic record
Abstract
Anticoagulation substantially reduces the risk of stroke in patients with atrial fibrillation (AF). However, recent studies have shown that up to 22%-36% of patients on anticoagulation will suffer an ischaemic stroke (IS). In this narrative review, we provide an overview of risk factors, mechanisms, management of acute IS and strategies for secondary prevention for patients with AF with stroke despite oral anticoagulation. For this paper, we reviewed available literature from important studies (randomised clinical trials, meta-analyses, reviews and case series) on patients with IS despite anticoagulation. We focused on recent studies that examined safety and efficacy of acute stroke treatments and evaluation and management strategies for secondary prevention. The literature review suggests that patients with AF with IS despite anticoagulation are a heterogeneous group with several possible mechanisms, which may include reduced or non-adherence to anticoagulation, competing non-cardioembolic stroke aetiologies or cardioembolic mechanisms separate from AF. The identification of one or more possible mechanisms of stroke despite anticoagulation may allow for a more targeted and individualised approach for secondary prevention. There are limited data to guide management in such patients, and strategies to prevent recurrent strokes include strict risk factor control and therapies targeting the most likely stroke mechanism. In cases where AF is suspected to be the culprit, clinical trials are needed to test the safety and efficacy of left atrial appendage occlusion plus anticoagulation versus continued anticoagulation alone.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".