2004
DOI: 10.1016/j.gaitpost.2003.10.002
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Classification of equinus in ambulatory children with cerebral palsy - discrimination between dynamic tightness and fixed contracture

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Cited by 33 publications
(22 citation statements)
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“…It is likely that these studies used a sample of convenience because this type of sampling involves the selection of the most accessible members of the target population [40]. Five studies [2,7,10,18,19] reported using a retrospective sample of convenience chosen from children who had already attended a health service such as a gait laboratory or orthopaedic outpatient unit. The main limitation of this sampling method is that the sample may be biased and not representative of the entire population of interest [40].…”
Section: Sampling Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…It is likely that these studies used a sample of convenience because this type of sampling involves the selection of the most accessible members of the target population [40]. Five studies [2,7,10,18,19] reported using a retrospective sample of convenience chosen from children who had already attended a health service such as a gait laboratory or orthopaedic outpatient unit. The main limitation of this sampling method is that the sample may be biased and not representative of the entire population of interest [40].…”
Section: Sampling Methodsmentioning
confidence: 99%
“…The diversity of gait deviations observed in children with CP has led to repeated efforts to develop gait classification systems to assist in diagnosis, clinical decision-making and communication [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Gait classifications may enable clinicians to differentiate gait patterns into clinically significant categories that assist with clinical decision-making.…”
Section: Introductionmentioning
confidence: 99%
“…Using a heuristic optimization algorithm to seek for the most discriminant, independent components in time series, it is possible to render the evaluation more robust [8]. Artificial neural networks [12][13][14][15] are a useful tool for classification of huge data sets and have the advantage of a very high classification rate. However, classification is not inherently transparent to the user of these networks, which poses major problems to the clinician during validation and interpretation of the classification rules and strongly hampers their clinical acceptance.…”
Section: Introductionmentioning
confidence: 99%
“…Understanding the causes of toewalking clearly aids the optimisation of treatment plans; for example, distinguishing between dynamic tightness and fixed contracture [10,23] in cerebral palsy patients has allowed improvement in management [21].…”
Section: Introductionmentioning
confidence: 99%