Optimizing Motor Recovery: Dual-Task Training versus Motor Relearning Program for Ambulatory Left-Hemiplegic Stroke Patients
Bibliographic record
Abstract
Background: Stroke is a significant cause of long-term disability, leading to chronic impairments of balance and gait. However, successful rehabilitation can help stroke survivors improve their mobility and quality of life. This study compares the effects of Dual-Task Training (DTT) and Motor Relearning Program (MRP) on dynamic balance and gait parameters in chronic stroke patients with left hemiplegia. Methods: A randomized, double-blinded controlled trial was done in a tertiary care hospital from March to August 2023. Through simple randomization, 40 subjects with chronic left hemiplegic stroke were allotted into either the DTT group or the MRP group. Both groups received 45-minute sessions three times weekly for 12 weeks. The primary outcomes measured were the 10-Meter Walk Test (10MWT) and the Timed Up and Go Test (TUG)—secondary outcomes related to gait parameters, step length, cadence, cycle time, and stride length. Statistical analyses involved paired and independent t-tests, with a set level of significance described as p<0.05. Results: The statistical improvements in the DTT group show in the gait speed (10MWT) and TUG scores, which are significantly better than in the MRP group (p<0.05). Likewise, the DTT group’s step length, cadence, cycle time, and stride length also improved significantly (p<0.05). Conclusion: The use of DTT significantly improves the dynamic balance and gait of chronic stroke patients with left hemiplegia compared to MRP. This underscores the effectiveness of DTT as a tool for rehabilitating motor function in stroke patients. Further research should be pursued to optimize its application and evaluate long-term outcomes. Keywords: Balance, Gait, Impairments, Stroke Rehabilitation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".