The association between atrial fibrillation and Alzheimer's disease: fact or fallacy? A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The association between atrial fibrillation and dementia has been described. Whether a specific association exists between atrial fibrillation and Alzheimer's disease remains uncertain. This study aims to assess the association between atrial fibrillation and Alzheimer's disease through a systematic review and meta-analysis of the literature. METHODS: An exhaustive search of electronic databases up to October 2018 was conducted. Studies that identified patients with and without atrial fibrillation as well as patients with and without Alzheimer's disease and reported results of at least one relevant outcome, including hazard ratio of the association between atrial fibrillation and Alzheimer's disease were included in this analysis. The hazard ratios and their confidence interval were then pooled using a DerSimonian and Laird random effects model. RESULTS: Six studies enrolling a total of 56 370 patients were included. At baseline, the mean or median ages ranged from 50 to 78 years with a subsequent follow-up of 3 to 25 years. The random-effect pooled analysis showed a hazard ratio of 1.30 (95% confidence interval 1.01-1.59) and the heterogeneity was not significant, I 48.1%. All of the included studies were rated as good quality. CONCLUSION: Pooled analysis suggest that patients with atrial fibrillation may be exposed to an increased risk of developing new onset of Alzheimer's disease. Given the relevant clinical implications, further studies are required to corroborate these findings.
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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 | 0.043 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.024 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".