Effect of cognitive training on selected gait parameters in patients with stroke
Bibliographic record
Abstract
Background. Not only may early cognitive rehabilitation help stroke patients with their cognitive impairment, but it can also help patients restore their capabilities to do everyday activities. There isn’t much data on how cognitive training affects spatiotemporal gait variables in stroke survivors. Objective. To examine the impact of cognitive training on selected gait parameters in stroke patients. Subjects and Methods. Forty male and female patients with mild ischemic chronic stroke, ranging in age from 45 to 60 years, were recruited and distributed into two equal groups at random (G1 and G2). The Montreal Cognitive Assessment Scale (MOCA) and the Rehacom system were utilized to evaluate the patient’s cognitive function. The Biodex gait trainer device was utilized to measure several gait variables for all patients. Rehacom cognitive training and a selected physiotherapy program were administered to the study group (G1). The similar selected physiotherapy program utilized for the G1 was applied to the control group (G2). For eight weeks, the treatment was administrated three sessions each week, day after day. All variables were evaluated before- and after-intervention. Results. The step length and walking speed, as well as the MOCA scale score of G1, were significantly higher after treatment than those of G2 (p = 0.001). The correlation between step length and median reaction time (MRT) was moderate negative significant correlation (r = – 0.698, p = 0.001). Conclusion. Cognitive training has a beneficial impact on improving the selected spatiotemporal gait parameters in stroke patients.
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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) | 0.001 | 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".