Experience of using the sensor treadmill to improve the functions of pacing, cognitive abilities and psychoemotional status in patients with ischemic stroke
Bibliographic record
Abstract
BACKGROUND: Stroke in developed countries continues to be the most important medical and social problem and occupy a leading position in the structure of morbidity and mortality. AIMS: To study the effect of the technique of robotic biomechanical rehabilitation on a sensory treadmill with built-in power platforms and biofeedback in patients with post-stroke spasticity on the main indicators of stride, cognitive function and psychoemotional status in the late recovery period. MATERIAL AND METHODS: The study included 80 patients who underwent ischemic stroke with movement disorders in the form of hemiparesis with increased muscle tone by the type of spasticity in the upper and lower extremities in the late recovery period, who were divided into 2 groups: the control group ― 40 people, who received standard drug therapy and medical rehabilitation, including medical massage, exercise therapy and kinesitherapy, and the main group ― 40 people, who, against the background of standard drug therapy and medical rehabilitation, underwent training on a sensory treadmill with built-in power platforms and biofeedback. Objective indicators of step function, subjective indicators of cognitive impairment (Montreal Cognitive Assessment Scale, MoCA) and psychoemotional defects (Hospital Anxiety and Depression Scale, HADS) were assessed. RESULTS: When analyzing the data after the course of treatment in the patients of the main group, the indicators of the parameters of the walking stereotype improved significantly compared to the data in the control group, the cognitive functions normalized, and the degree of psychoemotional defect decreased, which was confirmed by the MoCA and HADS scales. CONCLUSION: The inclusion of training on a sensory treadmill with built-in power platforms and biofeedback in the standard complex of medical rehabilitation of patients who have suffered an ischemic stroke with movement disorders in the form of hemiparesis in the lower extremities in the late recovery period contributes to a significant improvement in the biomechanical indicators of stride, cognitive functions and a decrease in the degree of psychoemotional impairment.
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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) | 0.002 | 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".