Title |
Predicting Developmental Status from 12 to 24 Months in Infants at Risk for Autism Spectrum Disorder: A Preliminary Report
|
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Published in |
Journal of Autism and Developmental Disorders, April 2012
|
DOI | 10.1007/s10803-012-1521-0 |
Pubmed ID | |
Authors |
Suzanne L. Macari, Daniel Campbell, Grace W. Gengoux, Celine A. Saulnier, Ami J. Klin, Katarzyna Chawarska |
Abstract |
The study examined whether performance profiles on individual items of the Toddler Module of the Autism Diagnostic Observation Schedule at 12 months are associated with developmental status at 24 months in infants at high and low risk for developing Autism Spectrum Disorder (ASD). A nonparametric decision-tree learning algorithm identified sets of 12-month predictors of developmental status at 24 months. Results suggest that identification of infants who are likely to exhibit symptoms of ASD at 24 months is complicated by variable patterns of symptom emergence. Fine-grained analyses linking specific profiles of strengths and deficits with specific patterns of symptom emergence will be necessary for further refinement of screening and diagnostic instruments for ASD in infancy. |
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Demographic breakdown
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Scientists | 1 | 100% |
Mendeley readers
Geographical breakdown
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Demographic breakdown
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Student > Ph. D. Student | 36 | 23% |
Researcher | 24 | 15% |
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Student > Doctoral Student | 15 | 9% |
Student > Bachelor | 7 | 4% |
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Unknown | 28 | 18% |
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