Gait Rehabilitation Using Hybrid Assistive Limb in Patients With Lower Limb Amputation: Protocol for a Single-Arm Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Lower limb amputation rates are increasing owing to aging and vascular diseases. However, no standardized rehabilitation protocol has been established for regaining walking ability. Conventional rehabilitation delays prosthetic gait training until 6-8 weeks post amputation, prolonging recovery and increasing medical expenses. OBJECTIVE: This study aims to evaluate the impact of neurorehabilitation using the Hybrid Assistive Limb (HAL) for Medical Use Lower Limb Type, developed by Japan's Tsukuba University and the robotics company Cyberdyne, on gait acquisition, improvement in activities of daily living (ADL), and enhancement of quality of life (QOL) in patients with lower limb amputation, and assess the effectiveness of early HAL-assisted gait training before and after amputation. METHODS: This single-arm trial will include 20 patients undergoing unilateral transfemoral or transtibial amputations. HAL-assisted gait training will be performed 5 days per week (30 minutes per session) until postoperative week 8. The primary outcome is a 2-minute walking distance, while secondary outcomes include muscle strength, balance, gait parameters, and ADL/QOL measures. Statistical analyses will be conducted using SPSS (IBM Corp), and results obtained at the following time points will be compared: preoperative, postintervention, and 6-month follow-up. RESULTS: The planned sample size is 20 patients, calculated using JMP version 12 (SAS Institute) based on an expected 1.5-fold improvement in 6-minute walk distance (effect size 0.8, α=.05, power=0.8). Analyses will be performed using SPSS version 15.0 (IBM Corp). The 2-minute walk test (primary outcome) and secondary outcomes will be compared at baseline, post intervention, and 6-month follow-up using 1-way analysis of variance or Kruskal-Wallis test, and P values <.05 and 95% CIs will be reported. CONCLUSIONS: This is the first study to apply HAL-assisted neurorehabilitation to patients with lower limb amputations. Early gait training may increase prosthetic gait acquisition rates, shorten rehabilitation and hospitalization periods, and reduce medical costs. If effective, this study may contribute to the development of a Japan-originated rehabilitation program and provide clinical evidence supporting broader HAL implementation for patients with lower limb amputations. TRIAL REGISTRATION: Japan Registry of Clinical Trials jRCTs062200031; https://jrct.mhlw.go.jp/latest-detail/jRCTs062200031. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/76509.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".