Meta-Analysis of the Relationship between Multiple Sclerosis and Migraine
Bibliographic record
Abstract
BACKGROUND: Studies investigating a proposed association between multiple sclerosis (MS) and migraine have produced conflicting results and a great range in the prevalence rate of migraine in MS patients. By meta-analysing all available data we aimed to establish an overall estimate of any association in order to more accurately inform clinicians and care-givers about a potential association between MS and migraine. METHODS: Pubmed and EMBASE were searched to identify suitable studies. Studies were included if they were a case-control study or cohort study in which controls were not reported to have another neurological condition, were available in English, and specified migraine as a headache sub-type. The odds ratio (OR) of migraine in MS patients vs. controls was calculated using the inverse variance with random effects model in Review Manager 5.1. RESULTS: Eight studies were selected for inclusion, yielding a total of 1864 MS patients and 261563 control subjects. We found a significant association between migraine and MS (OR = 2.60, 95% CI 1.12-6.04), although there was significant heterogeneity. Sensitivity analysis showed that migraine without aura was associated with MS OR = 2.29 (95% CI 1.14-4.58), with no significant heterogeneity. CONCLUSIONS: MS patients are more than twice as likely to report migraine as controls. Care providers should be alerted to ask MS patients about migraine in order to treat it and potentially improve quality of life. Future work should further investigate the temporal relationship of this association and relationship to the clinical characteristics of MS.
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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 | 0.023 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.054 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".