Temporal association between atrial fibrillation and ischemic stroke: Systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Paroxysmal atrial fibrillation (PAF) is strongly associated with ischemic stroke. Continuous cardiac implantable electronic devices (CIEDs) can assess PAF episodes over prolonged periods. Studies that attempted to find a temporal association between PAF and ischemic stroke were inconclusive. Thus, we performed a systematic review and meta-analysis to assess this relationship. AIMS: This study aimed to assess the temporal association between AF episodes and stroke within 30 days of the arrhythmic episode. The secondary outcome is a temporal association within a 90-day period. SUMMARY OF REVIEW: A total of 2804 studies that discussed the temporal relationship between PAF and ischemic stroke were screened, and 7 studies were included in the meta-analysis. Among the 4041 patients included in these studies, there were 138 patients with device detected PAF episodes and stroke. Four studies used a 30-day window for temporality and the pooled odds ratio (OR) showed a significant association (OR 4.11 (95% CI 1.03-16.40)). The three studies reporting on AF and stroke within a 90-day window did not find a significant temporal relationship (OR 0.43 (95% CI 0.13-1.41)). Finally, the pooled result of those seven studies did not show a significant association (OR 1.51 (95% CI 0.44-5.17)). CONCLUSION: This meta-analysis supports a temporal relationship between PAF and ischemic stroke within a 30-day window. Establishing this relationship is important for individualized risk prediction and targeted anticoagulation treatment. DATA ACCESS STATEMENT: The data will be made available upon reasonable request.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.036 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".