CLINICAL AND NEUROPHYSIOLOGICAL PECULIARITIS OF MOTOR DEFICIT AND CORRECTION OF COGNITIVE DISORDERS IN CEREBRAL ISCHEMIC HEMISPHERIC POST-STROKE PATIENTS
Bibliographic record
Abstract
Cerebral strokes often lead to severe consequences, such as motor disorders and dysfunctions of higher cerebral functions that affect the quality of life. Well-timed diagnosis and correction of the motor and cognitive disorders helps to improve the neurorehabilitation program for post-stroke patients. A total of 45 patients with hemispheric ischemic stroke were examined using a comprehensive approach that included clinical-neurological assessment, neurophysiological testing, and psychodiagnostic evaluation. The following standardized scales were employed: the National Institutes of Health Stroke Scale (NIHSS), Action Research Arm Test (ARAT), Modified Ashworth Scale (MAS), Montreal Cognitive Assessment (MoCA), and the Hospital Anxiety and Depression Scale (HADS). The study revealed an increase in the number of patients presenting with mild to moderate motor deficits, alongside a decrease in those with moderate to severe motor impairments, following complex therapy that included Bilobil Intense. Central motor neuron damage was indicated by significant alterations in electroneuromyographic parameters, particularly reductions in the amplitude of the M-response and H-reflex. At 12 months post-stroke, a progression of cognitive impairment as well as symptoms of anxiety and depression was observed. Furthermore, the severity of motor deficits showed a positive correlation with the intensity of anxiety-depressive symptoms.
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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 | 0.001 |
| 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".