Left atrial appendage occlusion for stroke prevention in patients with atrial fibrillation: a systematic review and network meta-analysis of randomized controlled trials
Bibliographic record
Abstract
INTRODUCTION: Atrial fibrillation (AF) is one of the leading causes of stroke. Risks associated with oral anticoagulation (OAC) limit adherence to recommended therapy. Left atrial appendage (LAA) occlusion is a treatment alternative in patients with AF. We performed a network meta-analysis (NMA) of randomized trials evaluating the efficacy of LAA occlusion compared with oral anticoagulant, antiplatelet, and placebo for stroke prevention. We also assessed the impact of LAA occlusion on mortality, major bleeding, and operative time. EVIDENCE ACQUISITION: We searched MEDLINE, EMBASE, PubMed, and Cochrane Library for randomized trials comparing percutaneous or surgical LAA occlusion with standard of care in AF patients. EVIDENCE SYNTHESIS: Conventional meta-analysis found no difference between groups for stroke (5 trials, 1285 patients;RR 0.78, 95% CI 0.47-1.29), and a significant reduction in mortality (5 trials, 1285 patients; RR 0.71, 95% CI 0.51-0.99) favouring LAA occlusion. NMA demonstrated a trend towards reduction in stroke (OR 0.84, 95% CrI 0.47-1.55) and mortality (OR 0.69, 95% CrI 0.44-1.10) for LAA occlusion versus warfarin, but no statistically significant effect. Statistical ranking curves placed LAA occlusion as the most efficacious treatment on the outcomes of stroke and mortality when compared to warfarin, aspirin, or placebo. No significant differences between groups were seen in major bleeding or operative time for surgical trials. The overall quality of the evidence was low as assessed by GRADE. CONCLUSIONS: LAA occlusion appears to preserve the benefits of OAC therapy for stroke prevention in patients with AF, but the current evidence is of low quality.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".