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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.
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 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 ...
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 ]
Screening tools include the Modified Checklist for Autism in Toddlers (M-CHAT), the Early Screening of Autistic Traits Questionnaire, and the First Year Inventory; initial data on M-CHAT and its predecessor, the Checklist for Autism in Toddlers (CHAT), on children aged 18–30 months suggests that it is best used in a clinical setting and that ...
Research-determined cut-offs identify the potential diagnosis of autism spectrum disorder, allowing a standardized assessment of autistic symptoms. The Autism Diagnostic Interview-Revised (ADI-R), a companion instrument, is a structured interview conducted with the parents of the referred individual to cover the subject's full developmental ...
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.
The term "high-functioning autism" was used in a manner similar to Asperger syndrome, another outdated classification.The defining characteristic recognized by psychologists was a significant delay in the development of early speech and language skills, before the age of three years. [12]
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