Short Walking Exercise Leads to Gait Changes and Muscle Fatigue in Children With Cerebral Palsy Who Walk With Jump Gait
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate kinematic changes and muscle fatigue in jump gait during a walking exercise and the relationship between kinematic changes and muscle fatigue and strength. DESIGN: This preliminary study included 10 children with cerebral palsy who walk with jump gait. Hip and knee maximal isometric muscle strength were measured using a dynamometer. Then, lower-limb kinematics and electromyography were collected while children walked continuously for 6 min at their self-selected speed. Electromyography median frequency and lower-limb joint angles were compared between the first and the sixth minutes of the walking exercise using t test and Wilcoxon rank test. The relationship between kinematic changes and muscle strength and changes in electromyography median frequency were assessed using correlation analyses. RESULTS: During stance, maximal knee flexion significantly increased at the sixth minute (P = 0.01) and was associated with knee extensor muscle weakness (ρ = -0.504, P = 0.03). Muscle fatigue was observed only in the gluteus medius muscle (P = 0.01). CONCLUSIONS: Children with cerebral palsy who walked with jump gait and who had knee extensor weakness were more prone to an increase in knee flexion during a continuous walk. The fatigue in the gluteus medius muscle suggests that physical intervention should target the endurance of this muscle to improve jump gait.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".