Literature DB >> 31951797

Hidden Aspects of the Research ADOS Are Bound to Affect Autism Science.

Elizabeth B Torres1, Richa Rai2, Sejal Mistry3, Brenda Gupta4.   

Abstract

The research-grade Autism Diagnostic Observational Schedule (ADOS) is a broadly used instrument that informs and steers much of the science of autism. Despite its broad use, little is known about the empirical variability inherently present in the scores of the ADOS scale or their appropriateness to define change and its rate, to repeatedly use this test to characterize neurodevelopmental trajectories. Here we examine the empirical distributions of research-grade ADOS scores from 1324 records in a cross-section of the population comprising participants with autism between five and 65 years of age. We find that these empirical distributions violate the theoretical requirements of normality and homogeneous variance, essential for independence between bias and sensitivity. Further, we assess a subset of 52 typical controls versus those with autism and find a lack of proper elements to characterize neurodevelopmental trajectories in a coping nervous system changing at nonuniform, nonlinear rates. Repeating the assessments over four visits in a subset of the participants with autism for whom verbal criteria retained the same appropriate ADOS modules over the time span of the four visits reveals that switching the clinician changes the cutoff scores and consequently influences the diagnosis, despite maintaining fidelity in the same test's modules, room conditions, and tasks' fluidity per visit. Given the changes in probability distribution shape and dispersion of these ADOS scores, the lack of appropriate metric spaces to define similarity measures to characterize change and the impact that these elements have on sensitivity-bias codependencies and on longitudinal tracking of autism, we invite a discussion on readjusting the use of this test for scientific purposes.

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Year:  2020        PMID: 31951797     DOI: 10.1162/neco_a_01263

Source DB:  PubMed          Journal:  Neural Comput        ISSN: 0899-7667            Impact factor:   2.026


  2 in total

Review 1.  A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder.

Authors:  Nadire Cavus; Abdulmalik A Lawan; Zurki Ibrahim; Abdullahi Dahiru; Sadiya Tahir; Usama Ishaq Abdulrazak; Adamu Hussaini
Journal:  J Pers Med       Date:  2021-04-14

2.  State and Trait Anxiety of Adolescents with Autism Spectrum Disorders.

Authors:  So Yoon Kim; Young Ah Kim; Da-Yea Song; Guiyoung Bong; Jong-Myeong Kim; Joo Hyun Kim; Hee Jeong Yoo
Journal:  Psychiatry Investig       Date:  2021-03-19       Impact factor: 2.505

  2 in total

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