Gait training using a hybrid assistive limb after botulinum toxin treatment for cerebral palsy: a case report
Bibliographic record
Abstract
(HAL; Cyberdyne, Tsukuba, Japan) is a wearable robot that assists patients based on their voluntary movements. We report gait training with HAL after botulinum toxin treatment for spasticity of the lower limb in cerebral palsy (CP). [Participant and Methods] The participant was a 36 year-old male with spastic diplegia due to periventricular leukomalacia, with Gross Motor Function Classification System (GMFCS) level II. HAL training was performed in 20-minute sessions (3 sessions/week for 4 weeks). The outcome measures were range of motion, spasticity, walking ability, muscle strength, gross motor function measure (GMFM), Canadian Occupational Performance Measure (COPM), and Pediatric Evaluation of Disability Inventory measured before, immediately after, and one, two, and three months after HAL training. [Results] No adverse events were observed during training. After the HAL intervention, gait speed, step length, cadence, 6-min walking distance (6MD), knee extension strength, GMFM, and COPM increased, and Physiological Cost Index declined. Three months post-intervention, gait speed, step length, cadence, 6MD, and GMFM remained higher than those observed within the first two months. [Conclusion] Gait training with HAL can be a safe and feasible method for patients with CP who undergo botulinum toxin treatment to improve walking ability and motor function.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".