Longitudinal development of gross motor function among <scp>D</scp>utch children and young adults with cerebral palsy: an investigation of motor growth curves
Bibliographic record
Abstract
AIM: The aim of this study was to describe patterns for gross motor development by level of severity in a Dutch population of individuals with cerebral palsy (CP). METHOD: This longitudinal study included 423 individuals (260 males, 163 females) with CP. The mean age at baseline was 9 years 6 months (SD 6y 2mo, range 1-22y). The level of severity of CP among participants, according to the Gross Motor Function Classification System (GMFCS), was 50% level I, 13% level II, 14% level III, 13% level IV, and 10% level V. Participants had been assessed up to four times with the Gross Motor Function Measure (GMFM-66) at 1- or 2-year intervals between 2002 and 2009. Data were analysed using non-linear mixed effects modelling. For each GMFCS level, patterns were created by contrasting a stable limit model (SLM) with a peak and decline model (PDM), followed by estimating limits and rates of gross motor development. RESULTS: The SLM showed a better fit for all GMFCS levels than the PDM. Within the SLM, significant differences between GMFCS levels were found for both the limits (higher values for lower GMFCS levels) and the rates (higher values for GMFCS levels I-II vs level IV and for GMFCS levels I-IV vs level V) of gross motor development. INTERPRETATION: The results validate the existence of five distinct patterns for gross motor development by level of severity of CP.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".