Migraine and the correlation between stroke: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: This study aims to analyze and evaluate the correlation between migraines and the onset of stroke, and further explore whether migraines increase the risk of stroke. METHODS: Two researchers independently conducted a comprehensive search of Chinese biomedical literature databases (CBM disc), PubMed, Embase, Cochrane Library, and Web of Science databases, using a combination of subject terms and free words. Literature that met the inclusion and exclusion criteria was selected, and quality assessment was performed using The Newcastle-Ottawa Scale (NOS). Data necessary for the study were extracted as effect size as needed, and meta-analysis was conducted using Review Manager 5.4 software and Stata 16 software to calculate the combined odds ratio and its 95% confidence interval. The relevant data were analyzed, and publication bias was evaluated. RESULTS: After conducting a meta-analysis of the 9 final included articles, the heterogeneity test showed chi2 = 10.7, df = 8 (P = .22), I2 = 25%. Therefore, a fixed-effect model was used for analysis. The combined odds ratio (OR) for the risk of stroke in migraine patients compared to non-migraine patients was 2.04, with a 95% confidence interval [1.73, 2.4], which was statistically significant. In the analysis of migraine with aura, the respective ORs were 2.32 with a 95% CI of [1.70-3.18] and 1.77 with a 95% CI of [1.34-2.33]. Subgroup analysis of female migraine patients and young migraine patients showed statistically significant results, with ORs of 2.26 (95% CI [1.67-3.05]) and 2.39 (95% CI [1.9-3.01]), respectively. The relevant literature and results were evaluated for publication bias and assessed using the NOS, indicating the reliability of the results. CONCLUSION: The results of the meta-analysis indicate that there is a certain relationship between migraine and the onset of stroke, and the results are relatively reliable. The analysis of migraine with aura shows that both the presence and absence of aura are associated with an increased risk of stroke. Subgroup analysis based on gender and age shows that the increased risk of stroke is associated with females and young individuals. However, due to the limited data in subgroup analysis, the above conclusions still require further research for validation.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".