Non-Vitamin K Antagonist Oral Anticoagulants (NOAC) forStroke Prevention in Atrial Fibrillation and Valvular HeartDisease – Systematic Review
Bibliographic record
Abstract
BACKGROUND: Non-vitamin K antagonist oral anticoagulants (NOACs) are commonly used for stroke prophylaxis in patients with atrial fibrillation (AF), but their efficacy and safety in patients with underlying valvular heart disease (VHD) is unknown. METHODS: A search of MEDLINE, CENTRAL, Embase, clinicaltrials.gov was performed with the terms direct oral antiocoagulants, new oral anticoagulants, DOAC, NOAC, rivaroxaban, apixaban, dabigatran, valvular heart disease, aortic stenosis, aortic regurgitation, mitral regurgitation, tricuspid stenosis, tricuspid regurgitation, pulmonary stenosis and pulmonary regurgitation. Only clinical studies with clinical endpoints that compared NOACs with warfarin in patients with AF and identified VHD were included. RESULTS: Four clinical studies were retrieved based on our search criteria. Subgroup analysis of the landmark trials comparing a NOAC to warfarin in AF patients with underlying VHD demonstrated that NOACs had similar or superior efficacy in stroke prevention compared to warfarin. The risk of bleeding with NOACs compared to warfarin in these patients yielded inconsistent results. CONCLUSION: Based on the available evidence, NOACs provide similar or superior stroke reduction compared to warfarin in patients with AF and VHD, especially in aortic valve disease and mitral regurgitation. The rate of major bleeding between NOACs and warfarin in this patient population is unclear.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".