Can MOCA Be Applied for Rough Cognitive Assessment in Patients with Epilepsy in Mongolia?
Bibliographic record
Abstract
Introduction: Epilepsy is a chronic neurological disorder, with cognitive impairment being one of its most significant comorbidities. While the majority of individuals with epilepsy maintain regular intellectual abilities, they are more likely to experience cognitive impairment compared to a healthy control group of the same age and educational level. Aim: This study aimed to assess cognitive impairment during epilepsy, particularly temporal lobe epilepsy, and to evaluate the effectiveness of using the Montreal Cognitive Assessment (MoCA) test for cognitive screening in individuals with epilepsy. Materials and methods: One hundred and fifty subjects were included between 2022 and 2023, which were divided into 50 people diagnosed with temporal lobe epilepsy (TLE), 50 people with other types of epilepsy according to the International League Against Epilepsy (ILAE), and 50 healthy controls without epilepsy (HC). Results: Significant differences were found in the total mean scores of the MoCA between TLE, other types of epilepsy, and healthy control groups (p = 0.000), particularly in visuospatial orientation, concentration, memory recall, abstraction, and language skills. Conclusions: Evaluating cognitive impairment in epilepsy involves comprehensive neuropsychological assessments, which have significantly advanced in recent years. Nevertheless, we consider the Montreal Cognitive Assessment (MoCA) test to be an appropriate initial screening tool for assessing cognitive impairment in epilepsy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".