A systematic review of the incidence and prevalence of sleep disorders and seizure disorders in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Several studies have suggested that comorbid neurologic disorders are more common than expected in multiple sclerosis (MS). OBJECTIVE: To estimate the incidence and prevalence of comorbid seizure disorders and sleep disorders in persons with MS and to evaluate the quality of studies included. METHODS: The PUBMED, EMBASE, Web of Knowledge, and SCOPUS databases, conference proceedings, and reference lists of retrieved articles were searched. Two reviewers independently screened abstracts to identify relevant articles, followed by full-text review of selected articles. We assessed included studies qualitatively and quantitatively (I² statistic), and conducted meta-analyses among population-based studies. RESULTS: We reviewed 32 studies regarding seizure disorders. Among population-based studies the incidence of seizure disorders was 2.28% (95% CI: 1.11-3.44%), while the prevalence was 3.09% (95% CI: 2.01-4.16%). For sleep disorders we evaluated 18 studies; none were population-based. The prevalence ranged from 0-1.6% for narcolepsy, 14.4-57.5% for restless legs syndrome, 2.22-3.2% for REM behavior disorder, and 7.14-58.1% for obstructive sleep apnea. CONCLUSION: This review suggests that seizure disorders and sleep disorders are common in MS, but highlights gaps in the epidemiological knowledge of these conditions in MS worldwide. Other than central-western Europe and North America, most regions are understudied.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.019 | 0.020 |
| 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) | 0.004 | 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".