Reference Curves for the Gross Motor Function Measure: Percentiles for Clinical Description and Tracking Over Time Among Children With Cerebral Palsy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Physical therapists frequently use the 66-item Gross Motor Function Measure (GMFM-66) with the Gross Motor Function Classification System (GMFCS) to examine gross motor function in children with cerebral palsy (CP). Until now, reference percentiles for this measure were not available. The aim of this study was to improve the clinical utility of this gross motor measure by developing cross-sectional reference percentiles for the GMFM-66 within levels of the GMFCS. SUBJECTS AND METHODS: A total of 1,940 motor measurements from 650 children with CP were used to develop percentiles. These observations were taken from a subsample, stratified by age and GMFCS, of those in a longitudinal cohort study reported in 2002. A standard LMS (skewness-median-coefficient of variation) method was used to develop cross-sectional reference percentiles. RESULTS: Reference curves were created for the GMFM-66 by age and GMFCS level, plotted at the 3rd, 5th, 10th, 25th, 50th, 75th, 90th, 95th, and 97th percentiles. The variability of change in children's percentiles over a 1-year interval also was investigated. DISCUSSION AND CONCLUSION: The reference percentiles extend the clinical utility of the GMFM-66 and GMFCS by providing for appropriate normative interpretation of GMFM-66 scores within GMFCS levels. When interpreting change in percentiles over time, therapists must carefully consider the large variability in change that is typical among children with CP. The use of percentiles should be supplemented by interpretation of the raw scores to understand change in function as well as relative standing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".