Effect of different oral anticoagulants on cognitive function in patients with atrial fibrillation: A Bayesian network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is 1 of the most common types of arrhythmias. At present, the treatment for patients with AF mainly includes oral anticoagulants (OACs). Studies have shown that OACs are associated with cognitive decline in patients with atrial fibrillation; however, there is a lack of relevant evidence. This study used Bayesian network meta-analysis (NMA) to investigate the effects of different oral anticoagulants on cognitive decline in patients with AF. METHODS: We systematically searched for clinical studies on oral anticoagulants in patients with AF in PubMed, Web of Science, Embase, and the Cochrane Library as of July 3, 2023. Cochrane's randomized controlled trial bias risk assessment tool and the Newcastle-Ottawa Scale were used to assess the bias risk of the included studies. The main outcome measure was decreased cognitive functioning. RESULTS: Ten studies were included, including 2 RCTs and 7 RCSs, including 882,847 patients with AF. Five oral anticoagulants and 2 anticoagulants were included: VKAs (especially warfarin), Dabigatran, Edoxaban, Rivaroxaban, Apixaban, and Aspirin, Clopidogrel. The results of the mesh meta-analysis showed that VKAs were superior to warfarin in reducing the risk of cognitive decline in patients with AF (OR = -1.19, 95% CI (-2.35, -0.06), P < .05) (Table 5). The top 3 drugs in terms of the probability of reducing the incidence of cognitive impairment in patients with AF with different oral anticoagulants were VKAs (87%), rivaroxaban (62.2%), and dabigatran (60.8%). CONCLUSION: Based on the results of this study, VKAs may be the best intervention measure for reducing the risk of cognitive decline in patients with AF. Owing to the limitations of this study, more high-quality randomized controlled trials with large sample sizes and multiple centers are required to provide more evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.064 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.069 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 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".