Investigating the Effect of Cognitive Rehabilitation on Cognitive Impairment Associated With Antiseizure Medications in Patients With Epilepsy
Bibliographic record
Abstract
Objective Most existing studies on cognitive rehabilitation in epilepsy focus on patients undergoing epilepsy surgery or classify interventions based on epilepsy type. This study aimed to determine whether antiseizure medications (ASMs) cause cognitive dysfunction in epilepsy patients by using neuropsychological assessments and auditory event-related potentials (ERPs), and whether cognitive rehabilitation can reduce this potential impact. Materials and Methods The study included patients scheduled to begin ASM monotherapy. All participants first underwent a face-to-face Montreal Cognitive Assessment (MoCA). Auditory ERPs including P300 and N200 latencies, and N2 to P3 peak-to-peak amplitudes were recorded in the electrophysiology laboratory. Patients were randomly divided into two groups: Group A (no cognitive rehabilitation) and Group B (received cognitive rehabilitation). After two months, both MoCA and auditory ERP measurements were repeated, and the results were statistically analyzed. Results In Group A, patients using carbamazepine (CBZ), zonisamide (ZNS), or valproic acid (VPA) showed a statistically significant decline in MoCA scores and auditory ERP results ( P < .05), suggesting a protective role of rehabilitation. For topiramate (TPM), cognitive decline was weakly significant even with rehabilitation ( P = .031)
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".