Study on the Correlation Between Patent Foramen Ovale and Migraine Based on Meta Analysis.
Bibliographic record
Abstract
Objective: This meta-analysis systematically investigates the association between Patent Foramen Ovale (PFO) and the prevalence of migraine. Our goal is to quantify this relationship and evaluate its implications for clinical practice and future research. Methods: An extensive literature search was carried out in various databases, such as PubMed, Embase, The Cochrane Library, Web of Science, CNKI, VIP, WanFang Data, and CBM, up to November 2023. The search focused on case-control, cross-sectional, and cohort studies examining the link between PFO and migraine. The literature screening and data extraction, based on predefined inclusion and exclusion criteria, were independently conducted by two reviewers. The studies' quality was evaluated using the Newcastle-Ottawa Scale (NOS), and RevMan 5.3 software was employed for the meta-analysis. Results: A total of 27 studies involving 8,875 participants were included in the meta-analysis. The results indicate a statistically significant association between PFO and migraine prevalence. Key findings include: (1) Overall, individuals with migraine had higher rates of PFO compared to healthy controls (OR = 3.22, 95% CI = 2.21 to 4.67, P < .00001). (2) The association was stronger in the Migraine with Aura group (OR = 3.69, 95% CI = 1.93 to 7.04, P < .0001) than in the Non-Migraine with Aura group (OR = 1.48, 95% CI = 1.09 to 2.00, P = .01). (3) The prevalence of PFO was notably higher in the Migraine with Aura group compared to the Non-Migraine with Aura group (OR = 2.32, 95% CI = 1.96 to 2.76, P < .00001). Conclusion: The analysis confirms a noteworthy correlation between PFO and migraine, underscoring the relationship and suggesting additional studies need to elucidate the underlying mechanisms and clinical ramifications.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.056 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".