The OlaMind screening tool for autism spectrum disorder in children aged 4-18 years.
Autism spectrum disorder (ASD) is a common neurodevelopmental condition whose diagnosis is often delayed. The feasibility of OlaMind, a new digital system to screen for ASD in verbal children and adolescents without intellectual disability, was tested. Parents in Israel used the instrument concernin...
| Publicado en: | International Journal of Child Health & Human Development Vol. 13; no. 4; pp. 395 - 406 |
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| Autor principal: | |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Nova Science Publishers, Inc.
2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150181085&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150181085 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19395965 903J jtl: International Journal of Child Health & Human Development issn: 19395965 maglogo: N pubinfo: dt: 2020 vid: 13 iid: 4 pid: 1040 pub: Nova Science Publishers, Inc. place: Hauppauge, New York artinfo: ui: 150181085 150181085 150181085 150181085 ppf: 395 ppct: 11 formats: fmt: @attributes: type: P tig: atl: The OlaMind screening tool for autism spectrum disorder in children aged 4-18 years. aug: au: Lavi, Rachelle sug: subj: Autism Spectrum Disorder Diagnosis Autism Spectrum Disorder Diagnosis Digital Technology Equipment and Supplies Clinical Assessment Tools Human Male Female Algorithms Descriptive Statistics Child, Preschool Child Adolescence Child, Preschool: 2-5 years Child: 6-12 years Adolescent: 13-18 years Male Female ab: Autism spectrum disorder (ASD) is a common neurodevelopmental condition whose diagnosis is often delayed. The feasibility of OlaMind, a new digital system to screen for ASD in verbal children and adolescents without intellectual disability, was tested. Parents in Israel used the instrument concerning their 4-18-year-old children with a doctor-given ASD diagnosis (ASD group: n = 33; 7 females) or without one (Control: n = 44; 20 females). A high proportion of the control group had parent-reported diagnoses of non-ASD conditions whose symptoms may resemble those of ASD (Other: n = 24; 9 females). Those who did not have such diagnoses were considered typically developing (TD: n = 20; 11 females). The algorithm classified 32 (97%) of ASD cases as exhibiting an ASD symptom pattern across DSM-5 clinical diagnostic domains and one 18-year-old male as exhibiting a non-ASD symptom pattern. The algorithm classified 75% of TD cases and 50% of Other cases as non-ASD. There was good correlation between the analytic groups and algorithm classifications. Two Other cases and one TD case were subsequently diagnosed with ASD or with multiple other conditions, respectively. Classification accuracy, sensitiveity, and specificity were 81%, 97%, and 66%, respectively in the full sample, improving to 93%, 97%, and 84%, respectively for the ASD and TD groups after accounting for subsequent known diagnoses. The results demonstrate the feasibility of the OlaMind algorithm. A full validation and reliability study is planned. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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