Non-vitamin K oral anticoagulants for secondary stroke prevention in patients with atrial fibrillation
Bibliographic record
Abstract
The aims of this article are to review the evidence regarding the use of non-vitamin K oral anticoagulants (NOACs) for secondary stroke prevention as compared to vitamin K antagonists in patients with atrial fibrillation (AF) and in patients with embolic strokes of uncertain source (ESUS), and when to initiate or resume anticoagulation after an ischaemic stroke or intracranial haemorrhage. Four large trials compared NOACs with warfarin in patients with AF. In our meta-analyses, the rate of all stroke or systemic embolism (SE) was 4.94% with NOACs vs. 5.73% with warfarin. Among the patients with AF and previous transient ischaemic attack or ischaemic stroke, the rate of haemorrhagic stroke was halved with a NOAC vs. warfarin, and the rate of major bleeding was 5.7% with a NOAC vs. 6.4% with warfarin. There was no significant difference in mortality. In a trial comparing apixaban with aspirin in patients with AF, the rate of stroke or SE was 2.4% at 1 year with apixaban vs. 9.2% at 1 year with aspirin and the rates of major bleeding were 4.1% with apixaban vs. 2.9% with aspirin. Data from registries confirmed the results from the randomized trials. Initiation or resumption of anticoagulation after ischaemic stroke or cerebral haemorrhage depends on the size and severity of stroke and the risk of recurrent bleeding. Two large trials tested the hypothesis that NOACs are more effective than 100 mg aspirin in patients with ESUS. Neither trial showed a significant benefit of the NOAC over aspirin. In the meta-analysis, the rate all stroke or SE was 4.94% with NOACs vs. 5.73% with warfarin and the rate of haemorrhagic stroke was halved with a NOAC. The four NOACs had broadly similar efficacy for the major outcomes in secondary stroke 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".