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The Modified Checklist for Autism in Toddlers (M-CHAT) is a psychological questionnaire that evaluates risk for autism spectrum disorder in children ages 16–30 months. The 20-question test is filled out by the parent, and a follow-up portion is available for children who are classified as medium- to high-risk for autism spectrum disorder.
The Checklist for Autism in Toddlers (CHAT) is a psychological questionnaire designed to evaluate risk for autism spectrum disorder in children ages 18–24 months. The 14-question test is filled out by the parent and a pediatrician or physician and takes approximately 5 minutes to complete. [ 1 ]
The Autism Treatment Evaluation Scale (ATEC) is a 77-item diagnostic assessment tool that was developed by Bernard Rimland and Stephen Edelson at the Autism Research Institute. The ATEC was originally designed to evaluate the effectiveness of autism treatments, but it may also be beneficial as a screening tool for children.
Among these measurements, the Autism Diagnostic Interview-Revised (ADI-R) and the Autism Diagnostic Observation Schedule (ADOS) are considered the "gold standards" for assessing autistic children. [ 24 ] [ 25 ] The ADI-R is a semi-structured parent interview that probes for symptoms of autism by evaluating a child's current behavior and ...
The Child and Adolescent Symptom Inventory (CASI) is a behavioral rating checklist created by Kenneth Gadow and Joyce Sprafkin that evaluates a range of behaviors related to common emotional and behavioral disorders identified in the Diagnostic and Statistical Manual of Mental Disorders (DSM), including attention deficit hyperactivity disorder, oppositional defiant disorder, conduct disorder ...
assess autism in children, adolescents, and adults The Autism Diagnostic Observation Schedule ( ADOS ) is a standardized diagnostic test for assessing autism spectrum disorder . The protocol consists of a series of structured and semi-structured tasks that involve social interaction between the examiner and the person under assessment.
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Achenbach used machine learning and principal component analysis when developing the ASEBA in order to cluster symptoms together when forming the assessment's eight categories. This approach ignored the syndrome clusters found in the DSM-I, instead relying on patterns found in case records of children with identified psychopathologies.
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