Untacted automated robotic upper-trunk- lower reciprocal locomotor training for knee osteoarthritis: A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Although millions of people with osteoarthritis (OA) have altered biomechanical alignment, movement, and knee joint pain during gait, there are no effective and sustainable interventions. To mitigate such impairments, we developed an untacted self-automated robotic and electromyography (EMG)-augmented upper-trunk-lower reciprocal locomotor training (SRGT) intervention. OBJECTIVE: To compare the effects of SRGT and conventional treadmill gait training (CTGT) on the medial knee joint space width (JSW), hip adduction moment (HAM), knee varus deformity, pain, and physical function in community-dwelling older adults with OA. METHODS: Older adults diagnosed with medial compartment knee OA (5 men, 35 women; mean age = 78.50 ± 9.10 years) were recruited and underwent either SRGT or CTGT, 30 min a day, 3 times a week, over a 4-week period. Outcome measurements included the JSW, HAM, knee varus angle (VA), and Western Ontario McMaster Universities osteoarthritis index (WOMAC). RESULTS: Analysis of covariance (ANCOVA) showed that SRGT ed to greater changes in medial knee JSW (p= 0.00001), HAM (p= 0.00001), VA (p= 0.00001), and WOMAC (p= 0.00001) scores. CONCLUSION: This study provides the first evidence for the long-term clinical and biomechanical effects of SRGT on JSW, knee joint kinematics, kinetics, and WOMAC scores in older adults with OA. Most importantly, self-automatic robotic gait training may be an alternative, effective, and sustainable treatment for the upper-trunk-lower reciprocal locomotor training in older adults with OA.
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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 | 0.004 |
| 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".