Migraine Headache in Patients with Allergic Rhinitis: A Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
Background: Migraine is a condition characterized by recurrent episodes of unilateral headache. Allergic rhinitis (AR) is an IgE-mediated inflammatory condition of the nasal mucosa that is triggered by exposure to allergens. Migraine and AR may share underlying immunological mechanisms, including histamine release and mast cell activation. Despite the growing interest in the immunological interplay between allergic conditions and neurological symptoms, the specific relationship between AR and migraine remains underexplored. Methods: PubMed, Scopus, and Web of Science were systematically searched to identify relevant studies. Pooled odds ratio (OR) and pooled risk ratio (RR) were calculated with 95% confidence intervals (CI) using a random-effects model. The Newcastle-Ottawa Scale (NOS) was used for quality assessment. Heterogeneity assessment and subgroup analysis were also performed. Results: Eleven studies involving 4,704,591 participants were included. The pooled OR for migraine in individuals with AR was 2.94 (95% CI: 2.02–4.29; p < 0.0001; I² = 95.62%). The pooled RR from two cohort studies was 2.27 (95% CI: 1.10–4.65; p = 0.026; I² = 99.72%). Subgroup analysis revealed significant differences in the pooled OR regarding the source of individuals with AR and the method of AR assessment, with a higher pooled OR in hospital patients (OR = 7.32) and when using skin tests (OR =6.93), respectively. Conclusion: Migraine headaches are significantly associated with AR, particularly in hospital settings and when objectivemethodsareusedforARdiagnosis.Thefindingsofthisstudyshouldbeinterpreted cautiously owing to the high heterogeneity.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".