Mobility development of children, adolescents and young adults with cerebral palsy in high-, and low-/middle-income countries: a scoping review
Bibliographic record
Abstract
PURPOSE: To review research on mobility development in children, adolescents and young adults with cerebral palsy (CP). METHODS: This scoping review included longitudinal studies on mobility development of children and young people (19-21 years) with CP. Findings were reported considering mobility capacity and performance of individuals with CP, observed in low- and middle- or high-income countries. The results were analyzed by two physicians and a mother of a child with CP, using Patient and Public Involvement (PPI) strategy. RESULTS: Eleven studies included 3,047 individuals with CP. Lower Gross Motor Function Classification Measure (GMFCS) levels were associated with better mobility capacity and performance. Additionally, the lower the GMFCS level, the more stability is achieved at older ages. Ten studies in high-income countries showed that mobility capacity stabilized before performance. The only study conducted in a low-income country showed a decline in mobility capacity in early adolescence. INTERPRETATIONS: The development of mobility capacity and performance may be related to the presence of different contextual factors in socioeconomically diverse countries. The findings of this review are important for sharing, discussing, and managing mobility development patterns with family members.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".