Development and validation of item sets to improve efficiency of administration of the 66-item Gross Motor Function Measure in children with cerebral palsy
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AIM: To develop an algorithmic approach to identify item sets of the 66-item version of the Gross Motor Function Measure (GMFM-66) to be administered to individual children, and to examine the validity of the algorithm for obtaining a GMFM-66 score. METHOD: An algorithmic approach was used to identify item sets of the GMFM-66 (GMFM-66-IS) using data from 95 males and 79 females with cerebral palsy (CP; mean age 14y 7mo, SD 1y 8mo, range 12y 7mo to 17y 8mo). The GMFM-66-IS scores were then validated using combined data from three Dutch studies involving 134 males and 92 females with CP (mean age 7y, SD 4y 6mo, range 1y 4mo to 13y 8mo), representing all levels of the Gross Motor Function Classification System. RESULTS: The final algorithm contains three decision items from the GMFM-66 that determine which one of four item sets to administer. The GMFM-66-IS has excellent agreement with the full GMFM-66 both at a single assessment (intraclass correlation coefficient [ICC]=0.994, 95% confidence intervals [CI] 0.993-0.996) and across repeat assessments (ICC=0.92, 95% CI 0.89-0.95). INTERPRETATION: The GMFM-66-IS is a promising alternative to the full GMFM-66. Users should be consistent in their choice of measure (GMFM-66 or GMFM-66-IS) on repeat testing and clearly identify which method was used.
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