Supplementary Material for: The Association Between Allergic Diseases and Migraine: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: This study aims to systematically review and summarize epidemiological evidence on the relationship between allergic diseases and migraine outcomes. Methods: This meta-analysis, which was registered with PROSPERO (CRD420250656492), employed data from PubMed, Embase, the Cochrane Library, and references from the studies included in the review. The search encompassed literature from the inception of these databases through Feb 24, 2025. We included observational studies investigating the association between allergic diseases and migraine. The risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). Pooled odds ratio (OR) with 95% confidence interval (CI) were calculated using a random-effects model. Results: A total of 10 studies encompassing 14,952,953 participants were included. The overall risk for migraine in patients with allergic diseases was 1.52 (95% CI: 1.40–1.65). Specifically, the meta-analysis revealed an OR for atopic dermatitis of 1.27(1.17–1.38), 1.49 (95% CI 1.32–1.68) for asthma, 2.16 (95% CI 1.43–3.24) for allergic rhinitis, and 1.74 (95% CI 1.43–2.10) for allergic conjunctivitis. Conclusion: The current meta-analysis suggests that allergic diseases is associated with an increased risk of developing migraines. However, further large-scale prospective cohort studies are required to validate the proposed association, considering the considerable heterogeneity observed in our analyses.
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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 | 0.008 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.651 | 0.036 |
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".