Cognitive impairments in patients with treatment resistant epilepsy and complex rehabilitation
Bibliographic record
Abstract
Background. The study of features of comorbid pathology in patients with epilepsy is of particular interest due to the high prevalence of this pathology and a significant impact on the quality of life of patients and their social adaptation. Aim. The aim of the research was to detect versatile cognitive impairments and affective disorders in epilepsy, and to study the results of cognitive training and psychoeducation. Materials and methods. The theoretical analysis of modern scientific researches in the field of cognitive and affective impairments during epilepsy was carried out. We studied the features of clinical and psychopathological manifestations in patients, suffering from epilepsy. The study covered 146patients (85 men and 61 women) who were in inpatient care. The following psychodiagnostic techniques were used: the MOCA test, the Toronto Cognitive Assessment (TorCA), the MiniMult test, the Münsterberg test, the quality of life scale, the Hamilton scale of depression and anxiety. Results. This publication offers the results of a study of cognitive and affective disorders the quality of life in patients who suffer from epilepsy and the results of online cognitive training and psychoeducation. We found cognitive decline in 88% of patients with epilepsy and improvement of cognitive functions by methods of non-pharmacological correction. Conclusions. Affective and cognitive disorders significantly affects the quality of life of patients, their ability to work and socialization. The conducted research showed that compared to the control group of healthy persons, patients with epilepsy showed improvement in their cognitive decline, anxiety and depressive disorders. Cognitive online training appeared to be effective for the patients with 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".