Effects of Virtual Reality–Assisted and Overground Gait Adaptation Training on Balance and Walking Ability in Stroke Patients
Bibliographic record
Abstract
OBJECTIVE: This study compared the effects of virtual reality-assisted gait adaptation training with the overground gait adaptation training on balance and walking in patients with stroke. METHODS: Fifty-four eligible patients were enrolled. All patients were randomly divided into a virtual reality and control group, with 27 patients in each group. The virtual reality group received virtual reality-assisted training on the treadmill, whereas the control group received overground training in a physical therapy room. After the intervention, patients were assessed using walking speed, obstacle avoidance ability, Timed Up and Go test, postural stability, and the Barthel Index. RESULTS: Significant improvements in walking speed, obstacle avoidance ability, Timed Up and Go test, and eye-opening center of pressure speed were observed after the intervention ( P < 0.05). No statistically significant differences were found in eye-closing center of pressure speed, tandem center of pressure speed, single-leg center of pressure speed, and Barthel Index ( P > 0.05). CONCLUSIONS: Stroke patients may benefit from virtual reality-assisted gait adaptation training in improving walking and static balance function and reducing the risk of falls.
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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".