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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".