Seizure history and cognitive dysfunction in people with multiple sclerosis
Bibliographic record
Abstract
Background: Seizures are associated with reduced cognition in the general population and worse outcomes in people with multiple sclerosis (pwMS). Yet, it remains unclear whether seizures are linked to cognitive dysfunction in pwMS. Objectives: To evaluate the connection between seizure history and poorer cognition in pwMS. Methods: A consecutive sample of 803 pwMS reported any prior seizures. Covariates included age, sex, Wechsler Test of Adult Reading scores, educational years, Expanded Disability Status Scale (EDSS) scores, disease duration, disease subtype, high-efficacy disease-modifying therapy use, Hospital Anxiety and Depression Scale scores for anxiety and depression and Modified Fatigue Impact Scale scores. Linear regression analyses, controlling for covariates, were undertaken to predict Minimal Assessment of Cognitive Function in MS scores from seizure history. Results: Mean age was 44.01 years ( SD = 11.58), 76.84% were female, and median EDSS was 2.0 (interquartile range (IQR) = 1.5–3.5). Accounting for covariates, people with seizures ( n = 43, 5.35%) performed worse than those without ( n = 760) on Judgement of Line Orientation (β = −0.09, p < 0.01), California Verbal Learning Test-II learning (β = −0.08, p < 0.01) and memory (β = −0.10, p < 0.01), Brief Visuospatial Memory Test-Revised learning (β = −0.08, p = 0.01) and memory (β = −0.07, p = 0.05), Symbol Digit Modalities Test (β = −0.06, p = 0.04), Paced Auditory Serial Addition Test (β = −0.10, p < 0.01) and Delis-Kaplan Executive Function System (β = −0.07, p = 0.02). Conclusions: A seizure history independently predicts reduced cognition in pwMS.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".