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Treatment As Usual (TAU) for Preschoolers with Autism: Insight from the Artificial Neural Networks Analyses
Objectives: The main aim is to use Auto-CM, a specific Artificial Neural Networks, in order to evaluate the natural relationships among outcome measure in a group preschoolers with autism engaged in a treatment as usual (TAU).
Methods: 61 preschoolers with ASD aged between 24 and 48 months were recruited at different centers in Italy. They were evaluated by blind researchers at baseline and after six months using ADOS-G, Griffiths Mental Developmental scales, and Vineland Adaptive Behavior scales. Parents filled out MacArthur Inventory, Social Communication Questionnaire, and Child Behavior Check List. All children were referred to community providers for available interventions.
Results: At endpoint, most of the children were still classified as having an ADOS-G classification of ASD. However, 21 (34.2%) passed from Autism to Autism Spectrum and 3 (4.2%) from Autism Spectrum to Non-Spectrum. Treatment effects were obtained for cognitive functioning, language, adaptive behavior, and child behavior, without differences between developmental-oriented and behavioral-oriented interventions. Parent involvement was a mediator for the best clinical outcome. Baseline low impairments of communication, language comprehension, and gesture were predictors of positive outcome. On the other hand Auto-CM system showed complex relationships between studied outcome variables.
Conclusions: Treatment as usual, composed of individual therapy plus school supported inclusion, may be an effective intervention in ASD. Better initial levels of communication in the child and parent involvement during treatment have an important role on positive outcome.