Gross Motor Development by Age and Functional Level in Children with Cerebral Palsy from 6 Months to 17 Years—A Norwegian Population-Based Registry Study
Bibliographic record
Abstract
Background: Cerebral palsy is a complex lifespan disability caused by a lesion to the immature brain. Evaluation of interventions for children with cerebral palsy requires valid and reliable outcome measures. Motor development curves and reference percentiles for The Gross Motor Function Measure (GMFM-66) are valuable tools for following, predicting, comparing, and evaluating changes in gross motor skills. The aims of this study were to create motor development curves with reference percentiles based on Norwegian data and compare them with published counterparts for Canadian children aged 2–21 years. Method: Prospective population-based cohort data from the Norwegian Quality and Surveillance Registry for Cerebral Palsy (NorCP) for 1206 children with 3612 GMFM-66 tests between 0.5 and 17.3 years of age. Median development by Gross Motor Function Classification System (GMFCS) levels was estimated using a generalized additive regression model with smoothed parameters for location, scale, and shape (based on the R GAMLSS library). To adjust for repeated individual measurements, we report the median curve of 100 random samples with only one observation per observed child. Results: The Norwegian motor development curves for GMFCS levels I–IV increase up to 7 years of age before flattening off, while GMFCS level V curves are relatively flat. Overall, both motor development curves and GMFM-66 percentiles are very similar to Canadian counterparts. Conclusions: The existing Canadian reference curves are valid also for Norway, working well for both clinical and research applications. However, Norwegian percentiles can be used from an earlier age.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".