Stroke Event Rates and the Optimal Antithrombotic Choice of Patients With Paroxysmal Atrial Fibrillation
Bibliographic record
Abstract
The risks of stroke or systemic embolism and major bleeding are considered similar between paroxysmal and sustained atrial fibrillation (AF), and warfarin has demonstrated superior efficacy to aspirin, irrespective of the AF type. However, with the advent of novel oral anticoagulants (NOACs) and antiplatelet agents, the optimal antithrombotic prophylaxis for paroxysmal AF remains unclear.We searched Medline, Embase, CENTRAL, and China Biology Medicine up to October week 1, 2015. Randomized controlled trials of AF patients assigned to NOACs, warfarin, or antiplatelets, with reports of outcomes stratified by the AF type, were included. A fixed-effects model was used if no statistically significant heterogeneity was indicated; otherwise, a random-effects model was used.Six studies of 69,990 nonvalvular AF patients with ≥1 risk factor for stroke were included. Postantithrombotic treatment, paroxysmal AF patients showed lower risks of stroke (risk ratio [RR], 0.72; 95% confidence interval [CI], 0.59-0.87), stroke or systemic embolism (RR, 0.74; 95% CI, 0.63-0.86), and all-cause mortality (RR, 0.75; 95% CI, 0.67-0.83), while the major bleeding risk was comparable (RR, 0.96; 95% CI, 0.85-1.08). We were unable to detect the superiority of anticoagulation over antiplatelets for paroxysmal AF (RR, 0.72; 95% CI, 0.43-1.23), while it was more effective than antiplatelets for sustained AF (RR, 0.42; 95% CI, 0.33-0.54). NOACs showed superior efficacy over warfarin and trended to show reduced major bleeding irrespective of the AF type.The AF type is a predictor for thromboembolism, and might be helpful in stroke risk stratification model in combination with other risk factors. With the appearance of novel anticoagulant and antiplatelet agents, the best antithrombotic choice for paroxysmal AF needs further exploration.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".