P.030 Association between multiple sclerosis and seizures: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Although seizures are a well-recognized phenomena in patients with multiple sclerosis (MS) with many observational studies reporting its prevalence and incidence, the relative risk of seizures or epilepsy in adults with MS compared to those without is not well-described. Methods: We systematically searched MEDLINE and Embase, from their inception to January 1, 2022, using keywords and database-specific terms. We included observational studies that reported risk of seizures or epilepsy in adults with MS and that in a comparison group, consisting of people without MS or the general population. We used a random-effects meta-analysis to report a pooled adjusted risk ratio (RR) of seizures in adults with MS compared to the comparison group. Results: We screened 8,750 articles and included 17 studies, totaling over 192,850 adults with MS of which 6064 (3.1%) had seizures. Compared to a comparison group, the pooled adjusted RR of seizures in adults with MS was 2.86 (95% CI, 2.35-3.47, I2 = 95.8%). Conclusions: MS should be considered an independent risk factor for seizures or epilepsy. Further research should help identify patients with MS who are at risk of seizures, to improve screening and treatment and in turn reduce the burden of epilepsy in this population.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.031 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".