Migraine Is Associated With Magnetic Resonance Imaging White Matter Abnormalities
Bibliographic record
Abstract
BACKGROUND: There is controversy as to whether migraine is associated with white matter abnormalities (WMAs) on magnetic resonance images. These abnormalities may be important as a risk factor for future stroke. Further, it is controversial whether any increased risk of WMAs is attributable to comorbidities such as vascular disease. METHODS: A meta-analysis of published case-control studies was undertaken to address the relationship between migraine and magnetic resonance imaging WMAs. Seven studies were identified. Data from studies reporting the incidence of magnetic resonance imaging WMAs in those with migraine and appropriate control populations were used to calculate odds ratios for WMAs in migraine for each study. A stratified meta-analysis was performed using studies that did and did not exclude subjects with disease comorbidities. RESULTS: The summary odds ratio shows that those with migraine are at increased risk for WMAs (odds ratio, 3.9 [95% confidence interval, 2.26-6.72]). The risk does not differ between studies that included subjects with comorbidities and those that did not. CONCLUSION: This meta-analysis demonstrates that subjects with migraine are at higher risk of having WMAs on magnetic resonance images than those without migraine. This increased risk is present even in younger individuals who do not have co-occurring cerebrovascular disease risk factors. Prospective studies are needed to determine whether the increased risk of stroke in migraine is mediated or foreshadowed by the presence of WMAs.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".