Comparing Diagnostic Outcomes of Autism Spectrum Disorder Using DSM-IV-TR and DSM-5 Criteria

Harstad, Elizabeth; Fogler, Jason; Sideridis, Georgios D.; Weas, Sarah; Mauras, Carrie; Barbaresi, William J.

doi:10.1007/s10803-014-2306-4

Cited by 38 publications

(23 citation statements)

References 65 publications

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“…A review article on prevalence of ASDs, including autistic disorder, AS, PDDNOS, and CDD found that, in comparison to autistic disorder and PDD-NOS, prevalence for AS was much lower and that the CDD was a rarity as a disorder [15]. The results of this review on epidemiological studies of ASDs are reflected in hospital based studies, including our study which have shown autistic disorder and PDD-NOS being more frequent diagnosis and Asperger disorder being diagnosed at a much lower rate whereas CDD diagnosed rarely [66]. About 85.45% of our subjects had comorbid mental retardation.…”

Section: Discussionsupporting

confidence: 58%

Clinico-Socio Demographic Profile of Children with Autism Spectrum Disorders from a Tertiary Care Hospital in Kashmir, India

Bhat¹,

Hussain²,

Qadir³

et al. 2019

J Child Dev Disord

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Background: In spite of advances in assessment and management of patients with Autism Spectrum Disorders (ASDs) in west, developing countries including India are lagging far behind in child psychiatry, let alone ASDs. The aims of our study were to find the socio-demographic and clinical profile of children with ASDs in a child psychiatry unit. Methods and Materials: This was a Cross sectional observational descriptive study conducted in Outpatient child psychiatry clinic. Semi-structured questionnaire was used to record the socio-demographic status. The diagnosis of ASDs was made on the basis of Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision (DSM IV-TR) after a thorough clinical assessment which was also confirmed by consultant in charge child psychiatry. Intelligence quotient was assessed by clinical psychologist. Descriptive statistical analysis was done and presented as frequencies and percentages. Results: A total of 55 patients were diagnosed with ASDs. Most of the patients were less than 9 years with 52.73% in 4-7 years age group and 21.82% in 7-9 year age group. Males (78.18%) outnumbered females. 52.73% belonged to Nuclear Family and 63.63% were from rural background. 70.91% were staying at home. Autism was the most frequent diagnosis in 61.82% of the patients followed by Pervasive Developmental Disorder not otherwise specified (PDD NOS) in 30.91%. Co-morbid mental retardation (MR) was present in 47(85.45%) of our subjects. Conclusion: 10.4% of children and adolescents with psychiatric disorders were having ASDs with autistic disorder and PDD NOS representing more than 90% of these cases. Despite the growing global attention for inclusive education to these children, only 3% of our cases were receiving inclusive education and about 70% of cases were staying at home.

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Section: Discussionsupporting

confidence: 58%

Clinico-Socio Demographic Profile of Children with Autism Spectrum Disorders from a Tertiary Care Hospital in Kashmir, India

Bhat¹,

Hussain²,

Qadir³

et al. 2019

J Child Dev Disord

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“…Frazier et al (2014) reported a twofactor model with one factor for each core ASD diagnostic criterion. Similarly, Harstad et al (2015) reported finding one ASD social impairment factor and one RRBs factor. Shuster et al (2014) reviewed 36 factor analytic studies of ASD symptoms and concluded that ASD social interaction impairment formed one factor, while RRBs formed a separate independent factor.…”

Section: Asd Social Impairment and Rrbs Form Independent Factorsmentioning

confidence: 93%

ASD Validity

Waterhouse

London

Gillberg

2016

Rev J Autism Dev Disord

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ASD research is at an important crossroads. The ASD diagnosis is important for assigning a child to early behavioral intervention and explaining a child's condition. But ASD research has not provided a diagnosis-specific medical treatment, or a consistent early predictor, or a unified life course. If the ASD diagnosis also lacks biological and construct validity, a shift away from studying ASD-defined samples would be warranted. Consequently, this paper reviews recent findings for the neurobiological validity of ASD, the construct validity of ASD diagnostic criteria, and the construct validity of ASD spectrum features. The findings reviewed indicate that the ASD diagnosis lacks biological and construct validity. The paper concludes with proposals for research going forward.Keywords DSM-5 . ASD . Autism . Diagnosis . Validity . Comorbidity . HeterogeneityThe goal of the DSM-3 nosology (American Psychiatric Association 1980) was to create reliable and standard categorical psychiatric diagnoses (Robins and Guze 1970). However, in the past 30 years, clinical, genetics, and neuroscience findings have revealed that the DSM diagnoses are not biologically valid. The National Institutes of Mental Health (NIMH) responded by proposing the Research Domain Criteria (RDoC) framework for a brain-based transdiagnostic psychiatric symptom nosology (Cuthbert and Insel 2013;Insel et al. 2010;Lilienfeld and Treadway 2016). Peterson (2015) and Weinberger et al. (2015) argued that the RDoC could not replace the DSM-5 psychiatric nosology (American Psychiatric Association 2013) or the parallel International Classification of Diseases (ICD) psychiatric nosology (World Health Organization 2012). But BFor the foreseeable future, RDoC is not envisioned as a system of psychiatric classification in its own right. Instead, in the near term, RDoC and DSM-ICD are expected to coexist. Nevertheless, RDoC is intended to provide scaffolding for a large-scale research program that will ultimately yield an alternative to DSM-ICD^ (Lilienfeld and Treadway 2016, p. 445).RDoC advocates accept that DSM-5/ICD psychiatric categories remain necessary in clinical practice, but argue that researchers should shift to RDoC study designs immediately. They assert that studying psychiatric categories lacking biological validity blocks the discovery of brain bases for psychopathology and thus cannot lead to effective medical treatments for specific psychiatric symptoms (Cuthbert and Insel 2013;Insel et al. 2010;Lilienfeld and Treadway 2016;Yee et al. 2015). Against this RDoC imperative for biological validity, Weinberger et al. (2015) countered that current DSM-5 psychiatric behavioral diagnoses were valid when they yielded effective medical treatment, clear prognosis, and a life course specific to a diagnosis.Autism spectrum disorder (ASD) research has been productive (Dawson 2016;de la Torre-Ubieta et al. 2016;Szatmari et al. 2016), but no ASD research findings have met the validity criteria of Weinberger et al. (2015). DSM-5 ASD research has found no s...

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“…This technology also advanced the screening approach over handheld devices such as mobile phones by mobile application based ASD which is also designed with respect to DSM-V standards in terms of autism detection [4]. The procedure of acquiring the better accuracy of subset along with optimum feature is known as dimensionality reduction [5].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Machine Learning Techniques on Autism Spectrum Disorder with Swarm Intelligence Based Feature Selection

Vijayalakshmi¹,

Vinayakamurthy²,

Anuradha³

2019

IJRTE

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This paper is a study on the various machine learning algorithms in order to perform ASD (Autism spectrum Disorder) as per the DSM-V standards. ASD occurs more frequently among children and in order to diagnose this with better accuracy, the study on binary firefly algorithm, a swarm intelligence based wrapper feature selection algorithm is used to obtain best results with optimum feature subsets. This paper will provide overall result after applying it to all types of machine learning models on supervised learning.

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Comparing Diagnostic Outcomes of Autism Spectrum Disorder Using DSM-IV-TR and DSM-5 Criteria

Cited by 38 publications

References 65 publications

Clinico-Socio Demographic Profile of Children with Autism Spectrum Disorders from a Tertiary Care Hospital in Kashmir, India

Clinico-Socio Demographic Profile of Children with Autism Spectrum Disorders from a Tertiary Care Hospital in Kashmir, India

ASD Validity

Optimization of Machine Learning Techniques on Autism Spectrum Disorder with Swarm Intelligence Based Feature Selection

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