Study protocol: functioning curves and trajectories for children and adolescents with cerebral palsy in Brazil – PartiCipa Brazil
Bibliographic record
Abstract
BACKGROUND: Gross motor development curves for children with Cerebral Palsy (CP), grouped by Gross Motor Function Classification System (GMFCS) levels, help health care professionals and parents to understand children's motor function prognosis. Although these curves are widely used in Brazil to guide clinical decision-making, they were developed with Canadian children with CP. Little is known about how these patterns evolve in children and adolescents with CP in low-income countries like Brazil. The PARTICIPA BRAZIL aims to: (i) to identify and draw a profile of functioning and disability of Brazilian children and adolescents with CP by classifying them, for descriptive purposes, with all five valid and reliable functional classifications systems (gross motor function, manual ability, communication function, visual and eating and drinking abilities); (ii) to create longitudinal trajectories capturing the mobility capacity of Brazilian children and adolescents with CP for each level of the GMFCS; (iii) to document longitudinal trajectories in the performance of activities and participation of Brazilian children and adolescents with CP across two functional classification systems: GMFCS and MACS (Manual Abilities Classification System); (iv) to document longitudinal trajectories of neuromusculoskeletal and movement-related functions and exercise tolerance functions of Brazilian children and adolescents with CP for each level of the GMFCS; and (v) to explore interrelationships among all ICF framework components and the five functional classification systems in Brazilian children and adolescents with CP. METHODS: We propose a multi-center, longitudinal, prospective cohort study with 750 Brazilian children and adolescents with CP from across the country. Participants will be classified according to five functional classification systems. Contextual factors, activity and participation, and body functions will be evaluated longitudinally and prospectively for four years. Nonlinear mixed-effects models for each of the five GMFCS and MACS levels will be created using test scores over time to create prognosis curves. To explore the interrelationships among ICF components, a multiple linear regression will be performed. DISCUSSION: The findings from this study will describe the level and nature of activities and levels of participation of children and youth with CP in Brazil. This will support evidence-based public policies to improve care to this population from childhood to adulthood, based on their prognosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".