Does square-stepping exercise improve balance in unilateral middle cerebral artery stroke survivors? A randomized controlled clinical trial
Bibliographic record
Abstract
Abstract Background Poststroke balance impairment adversely affects rehabilitation outcomes in chronic stroke survivors. Balance impairment heightens the risk of falls after stroke with negative consequences such as fall-related injuries including fracture, fear of falling, depression, social isolation, and even mortality. Objectives This study investigates the effectiveness of the square-stepping exercise as a balance training strategy for reducing the risk of falls among unilateral middle cerebral artery stroke survivors. Materials and methods In this randomized controlled trial, 42 chronic stroke survivors between the age group of 50 and 70 years were enrolled and assigned into 2 groups. Stroke survivors in the control group received conventional therapy, and the intervention group received conventional therapy and square-stepping exercise. Berg Balance Scale, timed up and go test, and Fall Efficacy Scale were included as outcome measures at baseline and post-intervention. Results The pre and post within-group analysis using paired t -test revealed a statistically significant difference in the Berg Balance Scale ( P = 0.000), timed up and go test ( P < 0.000), and Fall Efficacy Scale ( P < 0.002) for participants in the intervention group. In between-group analysis using an independent t -test, square-stepping exercise participants demonstrated significant improvement for the Berg Balance Scale ( P < 0.03). There are no significant changes in dynamic balance and fall risk outcomes between the groups. Conclusion Combining square-stepping exercises in conventional rehabilitation could be beneficial in improving balance among chronic stroke survivors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".