Child Study Center, Yale University School of Medicine, New Haven, CT, USA.
J Autism Dev Disord. 2012 Dec;42(12):2636-47. doi: 10.1007/s10803-012-1521-0.
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.
本研究旨在探讨在自闭症诊断观察量表(Autism Diagnostic Observation Schedule,ADOS)12 个月的幼儿模块中,个体项目的表现模式是否与高、低自闭症谱系障碍(Autism Spectrum Disorder,ASD)发病风险的婴儿在 24 个月时的发育状况相关。非参数决策树学习算法确定了一组 12 个月时预测 24 个月时发育状况的预测因子。结果表明,识别在 24 个月时可能表现出自闭症症状的婴儿是复杂的,因为症状出现的模式存在差异。将特定的优势和缺陷模式与特定的症状出现模式联系起来的精细化分析,对于进一步完善婴幼儿自闭症的筛查和诊断工具是必要的。