The effect of different oral anticoagulants on cognitive function in patients with atrial fibrillation: a Bayesian network meta-analysis
Bibliographic record
Abstract
Abstract Objective Atrial fibrillation (AF) is one of the most common arrhythmias. At present, the treatment for patients with atrial fibrillation mainly includes oral anticoagulants (OACs). Studies have shown that OACs are associated with cognitive decline in patients with atrial fibrillation, but there is a lack of relevant evidence. This study used Bayesian network meta-analysis to investigate the effects of different oral anticoagulants on cognitive decline in patients with atrial fibrillation. Method We systematically searched the clinical studies of oral anticoagulants on patients with atrial fibrillation included in PubMed, Web of Science, Embase, and Cochrane library as of July 3, 2023. Use Cochrane's randomized controlled trial bias risk assessment tool and Newcastle Ottawa Scale (NOS) to assess the bias risk of the included studies. The main outcome measure was a decrease in cognitive function. Result A total of 10 studies were included, including two RCTs and seven RCSs, including 882847 patients with atrial fibrillation. Including 8 oral anticoagulants: VKAs, Warfarin, Aspirin, Clopidogrel, Dabigatran, Edoxaban, Rivaroxaban, and Apixaban. The results of the mesh meta-analysis showed that VKAs were superior in reducing the risk of cognitive decline in patients with atrial fibrillation compared to warfarin (OR=-1.19, 95% CI (-2.35, -0.06), P < 0.05) (Table 4). The top three in terms of probability of reducing the incidence of cognitive impairment in patients with atrial fibrillation with different oral anticoagulants are VKAs (87%), Rivaroxaban (62.2%), and Dabigatran (60.8%). Conclusion Based on the results of this study, VKAs may be the potential best intervention measures that can reduce the risk of cognitive decline in patients with atrial fibrillation. Due to the limitations of this study, more high-quality randomized controlled trials with large samples and multiple centers are needed in the future to provide more evidence.
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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 | 0.031 | 0.052 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.062 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".