Growth and Health in Children With Moderate-to-Severe Cerebral Palsy
Bibliographic record
Abstract
BACKGROUND: Children with cerebral palsy frequently grow poorly. The purpose of this study was to describe observed growth patterns and their relationship to health and social participation in a representative sample of children with moderate-severe cerebral palsy. METHODS: In a 6-site, multicentered, region-based cross-sectional study, multiple sources were used to identify children with moderate or severe cerebral palsy. There were 273 children enrolled, 58% male, 71% white, with Gross Motor Function Classification System levels III (22%), IV (25%), or V (53%). Anthropometric measures included: weight, knee height, upper arm length, midupper arm muscle area, triceps skinfold, and subscapular skinfold. Intraobserver and interobserver reliability was established. Health care use (days in bed, days in hospital, and visits to doctor or emergency department) and social participation (days missed of school or of usual activities for child and family) over the preceding 4 weeks were measured by questionnaire. Growth curves were developed and z scores calculated for each of the 6 measures. Cluster analysis methodology was then used to create 3 distinct groups of subjects based on average z scores across the 6 measures chosen to provide an overview of growth. RESULTS: Gender-specific growth curves with 10th, 25th, 50th, 75th, and 90th percentiles for each of the 6 measurements were created. Cluster analyses identified 3 clusters of subjects based on their average z scores for these measures. The subjects with the best growth had fewest days of health care use and fewest days of social participation missed, and the subjects with the worst growth had the most days of health care use and most days of participation missed. CONCLUSIONS: Growth patterns in children with cerebral palsy were associated with their overall health and social participation. The role of these cerebral palsy-specific growth curves in clinical decision-making will require further study.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".