Developmental Trajectories and Reference Percentiles for Range of Motion, Endurance, and Muscle Strength of Children With Cerebral Palsy
Bibliographic record
Abstract
BACKGROUND: Children with cerebral palsy (CP) frequently present with secondary impairments in spinal alignment and extremity range of motion, endurance for activity, and muscle strength. Creation of developmental trajectories for these impairments will help guide clinical decision-making. OBJECTIVE: For children in each level of the Gross Motor Function Classification System (GMFCS) this study aimed to: (1) create longitudinal developmental trajectories for range of motion (Spinal Alignment and Range of Motion Measures [SAROMM]), endurance (Early Activity Scale for Endurance [EASE]), and functional strength (Functional Strength Assessment [FSA]); and (2) develop age-specific reference percentiles and amount of change typical over 1 year for these outcomes. DESIGN: This study used a longitudinal cohort design. METHODS: Participants comprised 708 children with CP across GMFCS levels, aged 18 months up to the 12th birthday, and their families. In 2 to 5 assessments every 6 months over 2 years, trained therapists performed the SAROMM and FSA, and parents completed the EASE questionnaire. For children in each GMFCS level, longitudinal trajectories using linear and nonlinear mixed-effects models from all visits, and reference percentiles using quantile regression from the first, 12-month, and 24-month visits were created for each measure. RESULTS: Longitudinal trajectories and percentile graphs for SAROMM, FSA, and EASE were primarily linear, with different performance scores among GMFCS levels. There was much variability in both longitudinal trajectories and percentiles within GMFCS levels. LIMITATIONS: Limitations included a convenience sample and varying numbers of participants assessed at each visit. CONCLUSIONS: The longitudinal trajectories and percentile graphs have application for monitoring how children with CP are performing and changing over time compared with other children with CP. The resources presented allow therapists and families to collaboratively make decisions about intervention activities targeted to children's unique needs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".