The outcomes of the computerized training of cognitive functions in patients with MCI in epilepsy
Bibliographic record
Abstract
Introduction Cognitive impairments have a considerable impact on the functioning of patients, their socialization and level of disability. Cognitive deficits significantly deteriorate the quality of patients’ life. Currently, the possibilities of pharmacological correction of cognitive disorders in patients with epilepsy are limited. Objectives Study of non-pharmacological program of cognitive disorder correction in patients with epilepsy and the assessment of its efficiency. Methods We have studied the features of clinical and psychopathological manifestations in patients suffering from epilepsy. The study included 146 patients with epilepsy (85 men and 61 women) who were receiving inpatient care. The following psychodiagnostic techniques were used: MOCA test, Mini Mult test, Münsterberg test, depression and Hamilton anxiety scale, quality of life scale. 63 patients received cognitive training online, of which 30 patients also used psychoeducation methods. Results According to the MoCA findings, patients with epilepsy showed cognitive decline, the average score was 20.72, whereas healthy persons’ average score was 27.36. The Quality of Life Scale: the average rate among all examined persons was 69.45 out of 100, 78.60 were the results of healthy persons. In patients with PG1, who used cognitive training and psychoeducation the results of the MoCA test showed an improvement in cognitive functions (1.4, p <0.001) and increased subjective assessment of quality of life (2.77, p <0.05). Conclusions The study of the use of cognitive training and psychoeducation in patients with epilepsy for cognitive functions, quality of life resulted in a positive outcome. Cognitive online training is an encouraging area in the rehabilitation of patients with cognitive decline. Disclosure No significant relationships.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".