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Autism Diagnostic Interview-Revised Within DSM-5 Framework: Test of Reliability and Validity in Chinese Children.

Kelly Yee-Ching LaiEmily C W YuenSe Fong HungPatrick W L Leung
Published in: Journal of autism and developmental disorders (2021)
This study examines the psychometric properties of the Autism Diagnostic Interview-Revised (ADI-R) in the context of DSM-5 in a sample of Chinese children. Using re-mapped ADI-R items and algorithms matched to DSM-5 criteria, and administering to children with autism spectrum disorder (ASD) with and without intellectual disability, attention-deficit hyperactivity disorder, and typically developing, it evidenced high sensitivity and specificity. However, similar to DSM-IV algorithm, the DSM-5 algorithms were better at classifying ASD among children with intellectual disability than among those without intellectual disability. With the DSM-5's recognition of the spectrum nature of ASD, the performance of the ADI-R can be improved by having finer gradations in the ADI-R scoring and adding more items on the restricted and repetitve behavior domain.
Keyphrases
  • intellectual disability
  • autism spectrum disorder
  • attention deficit hyperactivity disorder
  • machine learning
  • young adults
  • psychometric properties
  • deep learning
  • working memory