Prevalence of migraine in individuals with functional seizures: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: This study aimed to understand the prevalence of migraine in patients with functional seizures in general, as well as compared to patients with epileptic seizures. METHODS: We conducted a literature search of the PubMed, Scopus, and Web of Science databases from database inception until July, 2024. We identified studies using an observational design and performed a meta-analysis to evaluate the association between migraines and functional seizures. We assessed the quality of the studies with the Newcastle-Ottawa Scale, and identified the pooled odds ratios (OR) and mean differences. Heterogeneity was investigated using the I² statistic, significance was determined using Cochran's test, and a post-hoc Begg's test was performed. This study was registered with PROSPERO (CRD42024558536). RESULTS: Of the 3248 studies identified, 13 studies (N = 2415) were eligible for inclusion. Out of 2415 patients with functional seizures, 763 (31.5 %) patients had comorbid migraine. The pooled OR for prevalence of migraine in individuals with functional seizures compared with those with epileptic seizures was 2.77 (95 % CI: 2.36-3.27), with a corresponding mean difference of 18 % ((95 % CI: 9 %-27 %). Quality assessment revealed moderate-to-high quality in all the included studies. CONCLUSION: This study revealed a high prevalence of migraine in patients with functional seizures. However, existing evidence is limited to a handful of observational studies. More studies are needed to evaluate the direction of the association and the clinical and therapeutic impact of migraine on functional seizures.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".