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Record W4415027300 · doi:10.1177/15500594251376071

Investigating the Effect of Cognitive Rehabilitation on Cognitive Impairment Associated With Antiseizure Medications in Patients With Epilepsy

2025· article· en· W4415027300 on OpenAlexaboutno aff
Akçay Övünç Karadaş, Javid Shafiyev, Ömer Karadaş, Çağla Karadaş, Uğur Burak Şimşek, Betül Özenç, Özlem Aksoy Özmenek

Bibliographic record

VenueClinical EEG and Neuroscience · 2025
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsEpilepsyCognitionMontreal Cognitive AssessmentNeuropsychologyRehabilitationCognitive rehabilitation therapyZonisamideCarbamazepineTopiramate

Abstract

fetched live from OpenAlex

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)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.370
Teacher spread0.350 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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