Migraine and atrial fibrillation: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Patients with migraines, particularly those with auras, may present with stroke. Atrial fibrillation is a known risk factor for stroke. With common pathophysiological factors between migraines and atrial fibrillation, we aimed to clarify the association between migraine and atrial fibrillation in this systematic review and meta-analysis. METHODS: A literature search was conducted in EMBASE, PubMed, Scopus and Cochrane electronic bibliographic databases from inception to 5 September 2022 with the following inclusion criteria: (a) cohort or cross-sectional studies; (b) studies that included only patients aged ≥18 years; and (c) studies that examined the association between atrial fibrillation and migraines. Exclusion criteria were case-control studies and the studies that included patients with previous diagnosis of atrial fibrillation or nonmigrainous headache. The Newcastle-Ottawa Scale was used to assess the quality of studies. RESULTS: Six studies were included, demonstrating a pooled prevalence of atrial fibrillation of 1.61% (95% confidence interval [CI] 0.51, 3.29) in migraine with aura and 1.32% (95% CI 0.17, 3.41) in migraine without aura. The overall prevalence of atrial fibrillation in migraine was 1.39% (95% CI 0.24, 3.46). CONCLUSION: In this systematic review and meta-analysis, the overall prevalence of atrial fibrillation in patients with migraine was low. Further studies are needed to clarify this relationship.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.008 | 0.008 |
| 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".