Impact of non-farmacological methods on improvement cognitive function in epilepsy
Bibliographic record
Abstract
Introduction The quality of life of patients with epilepsy, their social activity and functioning depends not only on the presence of epileptic seizures, but also on the level of cognitive decline. Objectives The object of our study is impact of non-pharmacological methods on cognitive functions, decreasing of which deteriorates social activity in patients with epilepsy. Methods We have studied the features of clinical and psychopathological manifestations in patients suffering from epilepsy. The study covered 27 patients who were in inpatient care. The following psychodiagnostic techniques were used: the Toronto Cognitive Assessment TorCA, the MOCA and the MiniMult tests, Patient Social Functioning Questionnaire. Currently, a group of patients with cognitive decline cause by epilepsy is undergoing remote cognitive training on one of an online platforms, under our observation. Correction occurs throughout regular daily performance, assessment of cognitive functions is carried out on three indicators: attention, memory and thinking Results The following results of the study were observed: initially decreased memory in 88,8 % patients and the level of cognitive decline were directly proportional to the duration of the illness, this category of patients has reduced activity and limited social contacts.The result of the use of cognitive training is an increase in all three indicators. Conclusions The results of the study indicate the need for further study of the features of cognitive disorders in epilepsy and the use of methods of psychotherapeutic correction.
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 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) | 0.002 | 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".