Running capacity in children with bilateral cerebral palsy: What are the biomechanical and neuromotor differences between runners and walkers?
Bibliographic record
Abstract
BACKGROUND: Running is a high-level locomotor activity requiring more from joints, muscles and a more complex interaction of the neuromuscular system than walking. High-level locomotor activity has the potential to shed light on motor function deficits that lower-level activity does not reveal. Therefore, the study aimed to compare biomechanical and neuromotor function between a group of children with bilateral cerebral palsy who are able and unable to run. METHODS: Retrospectively, children with bilateral cerebral palsy aged between 6 and 18 years who completed a clinical gait analysis between 2006 and 2019 were included. Participants were categorized as walkers or runners based on the presence of a double floating phase. Spasticity, selectivity, muscle weakness, and passive range of motion of the lower limbs were measured and dichotomized as «normal» or «abnormal» based on reference values. Functional tasks reflecting balance (standing on one leg) and power (single leg and two-legged jumps) were realized and evaluated as failure or success. FINDINGS: 75 children with bilateral cerebral palsy (53 runners/22 walkers) were included. Children classified as runners were stronger (hip flexors, p = 0.006; hip abductors, p = 0.022; knee flexors, p = 0.001; dorsiflexors, p = 0.014), had greater selectivity (hip flexors, p = 0.011; dorsiflexors, p = 0.001; plantiflexors, p = 0.043) and lower spasticity at the knee extensors (p = 0.045). No differences were observed in the passive range of motion between the two groups. Children classified as runners performed better at all tasks of balance and power (p < 0.05). INTERPRETATION: Flexors muscles strength and selectivity and knee extensor spasticity are key points for running ability in children with bilateral cerebral palsy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".