Asymptomatic atrial fibrillation burden and thromboembolic events: piecing evidence together
Bibliographic record
Abstract
BACKGROUND: Contributory evidence on a direct association between asymptomatic atrial fibrillation (AF) burden and thromboembolic events is conflicting and contradictory. The aim of the article is to gather evidence available for a direct correlation between burden and stroke. METHODS: A literature search was performed to capture studies reporting data on the impact of asymptomatic AF burden on the risk of stroke. Data was then extracted from each included study including burden of AF, hazard ratio (HR) for stroke, and CHADS2 score. A random effects meta-analysis was carried out on the log-transformed HRs for different subgroups of AF burden. A meta-regression was performed on the two variables: burden of asymptomatic AF and CHADS2 score. RESULTS: The random-effect pooled analysis performed on a single subgroup of the six studies reporting data on HR, showed a HR of 2.150 (95% CI 1.523-3.003) for stroke during asymptomatic AF compared to sinus rhythm. At univariate meta-regression, no correlation was detected between burden of asymptomatic AF and HR for stroke (p-value 0,874). When CHADS2 score was included in the regression model as a covariate, no significant association was detected (p-value 0,939). CONCLUSION: A direct correlation between burden of asymptomatic AF and HR for stroke cannot be detected in our pooled analysis. However, due to the limitations acknowledged in the analysis, our findings need to be confirmed in large cohort studies.
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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 | 0.019 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".