The epidemiology of primary headaches in patients with multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: Recent studies have shown a pathophysiologic link between headache and multiple sclerosis (MS), but the prevalence of primary headaches among patients with MS differs substantially across studies. This meta-analysis aimed to comprehensively gather available evidence to estimate the prevalence of primary headaches among patients with MS. METHOD: We systematically searched the electronic databases including PubMed, Embase, and Scopus for cohort, case-control, cross-sectional studies that measured the prevalence of headache among patients with MS. Two reviewers independently screened titles and abstracts to identify the eligible studies and the full texts of the included studies were reviewed. Newcastle-Ottawa Scale (NOS) was used to assess the risk of bias of the included literatures. We then conducted a meta-analysis using Stata Software 15.0 to calculate the pooled prevalence of headaches among patients with MS and assess the source of heterogeneity. RESULTS: of 82.1% (p < .001). Both a visual inspection of the funnel plot and Egger' regression tests revealed no significant publication bias (p = .44). The pooled estimated prevalence of migraine (55%) was higher in comparison with that of tension-type headache (20%). The prevalence of migraine subtype was 16% and 10% for migraine without aura and migraine with aura, respectively. The pooled prevalence of primary headache in case-control group (57%) was approximately in line with the cross-sectional group (56%). CONCLUSION: The overall prevalence of primary headaches among patients with MS was considerably high. Clinical screening of headache among patients with MS will be helpful to formulate an individualized treatment plans and alleviate the physical and mental impact of the disease.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".