Effectiveness of a Walking Program Involving the Hybrid Assistive Limb Robotic Exoskeleton Suit for Improving Walking Ability in Stroke Patients: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Gait disturbance often occurs in stroke survivors. Recovery of walking function is challenging, as some gait disturbance due to hemiparesis often remains even after rehabilitation therapy, presenting a major obstacle towards regaining activities-of-daily-living performance and achieving social reintegration. OBJECTIVE: This study aims to clarify the effectiveness of a walking program involving the wearable Hybrid Assistive Limb (HAL-TS01) robotic exoskeleton for improving walking ability in stroke patients with hemiparesis and stagnant recovery despite ongoing rehabilitation. METHODS: This is a multicenter, randomized, parallel-group, controlled study (HAL group, n=27; control group, n=27). The study period includes preintervention observation (until stagnant recovery), intervention (HAL-based walking therapy or conventional rehabilitation; 5 weeks), and postintervention observation (2 weeks). Following provision of informed consent and primary registration, the patients undergo conventional rehabilitation for preintervention observation, during which the recovery of walking ability is monitored to identify patients with stagnant recovery (based on weekly assessments using the 10-meter maximum walking speed [MWS] test). Patients with an MWS of 30-60 m/minute and insufficient weekly improvement in MWS undergo secondary registration and are randomly assigned to undergo HAL-based walking therapy (HAL group) or conventional rehabilitation (control group). The primary outcome is the change in MWS from baseline to the end of the 5-week intervention. RESULTS: This study began in November 2016 and is being conducted at 15 participating facilities in Japan. CONCLUSIONS: Assessments of walking ability vary greatly and it is difficult to define the threshold for significant differences. To reduce such variability, our study involves conducting conventional rehabilitation to the point of saturation before starting the intervention. Stagnation in the recovery of walking ability despite conventional rehabilitation highlights the limits of current medical care. The present study may bring evidence that HAL-based therapy can overcome such limitations and induce added recovery of walking ability, which would promote the use of HAL technology in the clinical setting. TRIAL REGISTRATION: UMIN Clinical Trials Registry UMIN000024805; https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000028545.
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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 | 0.018 | 0.007 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".