Evidence map›Paper›PMID 40103289›Full record

ArticleJournal of child psychology and psychiatry, and allied disciplines2025

DSM-5 based algorithms for the Autism Diagnostic Interview-Revised for children ages 4-17 years.

Linnea A Lampinen, Shuting Zheng, Lindsay Olson, Vanessa H Bal, Audrey E Thurm, Amy N Esler, Stephen M Kanne, So Hyun Kim, Catherine Lord, China Parenteau and 4 more

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In one paragraph

Article in Journal of child psychology and psychiatry, and allied disciplines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Artificial Intelligence Methods in Early Detection of Autism Spectrum Disorder: A DSM-5 Criterion-Based Systematic Review.Autism research : official journal of the International Society for Autism Research · 2026
    Pooled it
  2. Diagnostic tests for autism spectrum disorder (ASD) in preschool children.The Cochrane database of systematic reviews · 2026
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Linnea A LampinenDepartment of Psychology, Rutgers University - New Brunswick, New Brunswick, NJ, USA.ORCID 0000-0003-4379-5782
Shuting ZhengDepartment of Psychiatry and Behavioral Sciences, University of Texas at Austin, Austin, TX, USA.
Lindsay OlsonDepartment of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, USA.
Vanessa H BalGraduate School of Applied and Professional Psychology, Rutgers University - New Brunswick, New Brunswick, NJ, USA.ORCID 0000-0003-0750-823X
Audrey E ThurmNeurodevelopmental and Behavioral Phenotyping Service, Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA.ORCID 0000-0002-0489-9485
Amy N EslerDepartment of Pediatrics, University of Minnesota, Minneapolis, MN, USA.
Stephen M KanneDepartment of Psychiatry, Weill Cornell Medicine, White Plains, NY, USA.
So Hyun KimSchool of Psychology, Korea University, Seoul, Republic of Korea.
Catherine LordSemel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA.
China ParenteauDepartment of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, USA.
Kerri P NowellDepartment of Health Psychology, Thompson Center for Autism & Neurodevelopment, University of Missouri, Columbia, MO, USA.
Jane E RobertsDepartment of Psychology, Carolina Autism and Neurodevelopment Research Center, University of South Carolina, Columbia, SC, USA.
Nicole TakahashiDepartment of Health Psychology, Thompson Center for Autism & Neurodevelopment, University of Missouri, Columbia, MO, USA.
Somer L BishopDepartment of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, USA.

Funding

Neurodevelopmental and Behavioral Phenotyping ZICMH002961 · NIMH · NATIONAL INSTITUTE OF MENTAL HEALTH · PI THURM, AUDREY · 2017 to 2025
$12.6M
Individualized approaches to determining likelihood of ASD casenessR01MH128288 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BISHOP, SOMER L. · 2022 to 2024
$2.4M
Intramural NIH HHS ZIC MH002961NIH HHS 1R01MH128288-01A1NIMH NIH HHS R01 MH128288NIMH NIH HHS ZICMH002961
6 · The paper itself

Abstract

backgroundThe Autism Diagnostic Interview, Revised (ADI-R) is a caregiver interview that is widely used as part of the diagnostic assessment for Autism Spectrum Disorder (ASD). Few large-scale studies have reported the sensitivity and specificity of the ADI-R algorithms, which are based on DSM-IV Autistic Disorder criteria. Kim and Lord (Journal of Autism and Developmental Disorders, 2012, 42, 82) developed revised DSM-5-based toddler algorithms, which are only applicable to children under 4 years. The current study developed DSM-5-based algorithms for children ages 4-17 years and examined their performance compared to clinical diagnosis and to the original DSM-IV-based algorithms.

methodsParticipants included 2,905 cases (2,144 ASD, 761 non-ASD) from clinical-research databanks. Children were clinically referred for ASD-related concerns or recruited for ASD-focused research projects, and their caregivers completed the ADI-R as part of a comprehensive diagnostic assessment. Items relevant to DSM-5 ASD criteria were selected for the new algorithms primarily based on their ability to discriminate ASD from non-ASD cases. Algorithms were created for individuals with and without reported use of phrase speech. Confirmatory factor analysis tested the fit of a DSM-5-based two-factor structure. ROC curve analyses examined the diagnostic accuracy of the revised algorithms compared to clinical diagnosis.

resultsThe two-factor structure of the revised ADI-R algorithms showed adequate fit. Sensitivity of the original ADI-R algorithm ranged from 74% to 96%, and specificity ranged from 38% to 83%. The revised DSM-5-based algorithms performed similarly or better, with sensitivity ranging from 77% to 99% and specificity ranging from 71% to 92%.

conclusionsIn this large sample aggregated from US clinical-research sites, the original ADI-R algorithm showed adequate diagnostic validity, with poorer specificity among individuals without phrase speech. The revised DSM-5-based algorithms introduced here performed comparably to the original algorithms, with improved specificity in individuals without phrase speech. These revised algorithms offer an alternative method for summarizing ASD symptoms in a DSM-5-compatible manner.

Indexed as

AlgorithmsAutism Spectrum DisorderDiagnostic and Statistical Manual of Mental DisordersInterview, PsychologicalPsychiatric Status Rating ScalesAdolescentChildChild, PreschoolFemaleHumansMaleSensitivity and SpecificityADI‐RAutismautism spectrum disorderdiagnosisdiagnostic instrumentssensitivityspecificity

Identifiers

PMID40103289
PMCPMC12350816

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.