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. 2011 Nov;53(11):1007-12.
doi: 10.1111/j.1469-8749.2011.04044.x.

Using the Gross Motor Function Classification System to describe patterns of motor severity in cerebral palsy

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Free article

Using the Gross Motor Function Classification System to describe patterns of motor severity in cerebral palsy

Susan M Reid et al. Dev Med Child Neurol. 2011 Nov.
Free article

Abstract

Aim: The aim of this study was to describe the distribution of motor severity levels and temporal trends in an Australian population cohort and to review the distribution of Gross Motor Function Classification System (GMFCS) levels across cerebral palsy (CP) registries worldwide.

Methods: Data were extracted from the Victorian Cerebral Palsy Register for 3312 individuals (1852 males, 1460 females; mean age 21y 2mo [SD 9 y 6 mo]) with non-postneonatally acquired CP, born between 1970 and 2003. The proportions of each motor severity level were calculated and logistic regression analyses were used to assess trends over time. A systematic review of the literature was undertaken and GMFCS data were extracted based on previously devised criteria. The proportions were plotted and the degree of heterogeneity was assessed for each level.

Results: Population data from Victoria suggested a proportional increase in mild motor impairment (GMFCS levels I/II) from 54% of all cases of CP in the 1970s to 61% in the 2000s. For nine CP registries worldwide, the mean proportions of each GMFCS level, from level I to V, were 34.2%, 25.6%, 11.5%, 13.7%, and 15.6% respectively. There was substantial heterogeneity between registries for all levels except level III.

Interpretation: Despite the usefulness and reported reliability of the GMFCS, substantial variability was found in the distribution of GMFCS levels between population registries, particularly between levels I and II, suggesting greater classification uncertainty between these levels. Further research would be useful to determine whether routine collection of extra clinical information may facilitate more reliable classification.

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