Model of personalized non-pharmacological program of cognitive disorder correction in patients with epilepsy and the assessment of its efficacy
Bibliographic record
Abstract
Cognitive dysfunction shows higher incidence in people, suffering from epilepsy, than in general population and most likely they are related to common patho-biological processes. The use of medication in patients with epilepsy for treating cognitive functions is limited due to the potential risk of more frequent seizures. Therefore, non-pharmacological methods of cognitive correction are becoming especially important for this category of patients.Aim. The aim of the research is to study the specifics of non-dementia cognitive disorders in patients with epilepsy and to develop a set of activities for their prevention and non-pharmacological correction.Materials and methods. The study included 146 patients (61 women and 85 men), their average age was 40,73. We applied clinical anamnestic, sociodemographic, clinical-psychopathological, psycho-diagnostic and statistical research methods. The following psycho-diagnostic techniques were used: Montreal Cognitive Assessment (MoCA), Munsterberg test, MiniMult test, Hamilton depression and anxiety rating scales (HRDS, HARS) and the quality of life scale. 63 patients completed a 3-month computerized online cognitive training with daily sessions, out of which 33 patients also participated in psychoeducational activities.Results. The findings of studying patients with epilepsy using the Montreal Cognitive Assessment (MoCA) showed low levels of all parameters while the total score in the patients with epilepsy with cognitive non-dementia disorders was 20,72 versus27,36 in healthy persons; the analysis of HRDS, HARS showed high anxiety and depression symptoms levels, lower quality of life level was also observed, the average indicator was 69,45, whereas in the control group the level of life quality was 78,60. There was marked adherence to cognitive training in the patients with epilepsy compared to the healthy group.Conclusion. Reliable improvement of cognitive functioning was found in the patients under consideration after 3 months of the training. The computerized cognitive functions training in current conditions showed its benefits owing to the opportunity to complete it online. It may be advised to be introduced as a part of the social rehabilitation system for patients with epilepsy, who have cognitive non-dementia disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".