Effects of lower‐extremity exoskeleton robot‐assisted dual‐task training versus walking training on gait and postural control after stroke: A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Therapeutic tools are critical for poststroke rehabilitation. The potential benefits of dual-task training assisted by a lower-extremity exoskeleton robot to enhance gait and postural control have yet to be studied. OBJECTIVE: To determine the effects of lower-extremity exoskeleton robot-assisted dual-task training on gait and postural control after stroke. DESIGN: Single-blind, randomized controlled trial. SETTING: Outpatient clinic and ward, Department of Rehabilitation Medicine, Huadong Hospital affiliated with Fudan University. PARTICIPANTS: Forty-four participants in the recovery and sequela phases of stroke with deficits in gait and postural control. INTERVENTIONS: Participants were randomly assigned to two groups: lower-extremity exoskeleton robot-assisted walking cognitive dual-task training (experimental group) or lower-extremity exoskeleton robot-assisted walking training (control group). Each participant received 40 minutes per intervention, 1 time per day, 6 times per week for 3 weeks. MAIN OUTCOME MEASURES: The primary outcome was gait variability performance. The secondary outcomes included the Timed Up and Go, Berg Balance Scale, Montreal Cognitive Assessment, Fugl-Meyer assessment of lower extremity, and International Classification of Functioning, Disability and Health-activities and participation assessment scale. RESULTS: Individuals who participated in exoskeleton robot-assisted walking cognitive dual-task training improved more than those in the control group in partial gait variability performance analysis, Timed Up and Go test, Berg Balance Scale, and Fugl-Meyer assessment for the lower extremities (p < .05). CONCLUSIONS: Compared to lower-extremity exoskeleton robot-assisted walking training, robot-assisted dual-task training improves gait and postural control, walking, balance, and lower extremity motor function in survivors of stroke.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.000 | 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".