Improved Scaling of the Gross Motor Function Measure for Children With Cerebral Palsy: Evidence of Reliability and Validity
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: This study examined the reliability, validity, and responsiveness to change of measurements obtained with a 66-item version of the Gross Motor Function Measure (GMFM-66) developed using Rasch analysis. SUBJECTS AND METHODS: The validity of measurements obtained with the GMFM-66 was assessed by examining the hierarchy of items and the GMFM-66 scores for different groups of children from a stratified random community-based sample of 537 children with cerebral palsy (CP). A subset of 228 children who had been reassessed at 12 months was used to test the hypothesis that children who are young (<5 years of age) and have "mild" CP will demonstrate greater change in GMFM-66 scores than children who are older ((5 years of age) and whose CP is more severe. Data from an additional 19 children with CP who were assessed twice, one week apart, were used to examine test-retest reliability. RESULTS: The overall changes in GMFM-66 scores over 12 months and a time ( severity ( age interaction supported our hypotheses. Test-retest reliability was high (intraclass correlation coefficient=.99). CONCLUSION AND DISCUSSION: This study demonstrated that the GMFM-66 has good psychometric properties. By providing a hierarchical structure and interval scaling, the GMFM-66 can provide a better understanding of motor development for children with CP than the 88 item GMFM and can improve the scoring and interpretation of data obtained with the GMFM.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".