Evidence map›Paper›PMID 41646735›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Behavioral Assessment Reliability in Clinical Phenotyping and Biomarker Research for Autism.

Enrique Perez-Benavides, Jueqi Wang, Zijian Chen, Stefen Beeler-Duden, Zachary Jackokes, John D Van Horn, Michael C Schatz, Kevin A Pelphrey, Archana Venkataraman

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Enrique Perez-BenavidesDepartment of Electrical and Computer Engineering, Boston University.
Jueqi WangDepartment of Electrical and Computer Engineering, Boston University.
Zijian ChenDepartment of Electrical and Computer Engineering, Boston University.
Stefen Beeler-DudenDepartment of Neurology, University of Virginia.
Zachary JackokesSchool of Data Science, University of Virginia.
John D Van HornSchool of Data Science, University of Virginia.ORCID 0000-0003-1537-0816
Michael C SchatzDepartment of Computer Science, Johns Hopkins University.
Kevin A PelphreyDepartment of Neurology, University of Virginia.
Archana VenkataramanDepartment of Electrical and Computer Engineering, Boston University.

Funding

Multimodal Developmental Neurogenetics of Females with ASDR01MH100028 · NIMH · YALE UNIVERSITY · PI Allison Elizabeth Jack, LAUREN KENWORTHY · 2012 to 2026
$37.8M
Statistical methods for structural and functional integration in multi-modal neuroimaging dataR01EB029977 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI CAFFO, BRIAN SCOTT · 2021 to 2024
$1.9M
A Modular Framework for Data-Driven Neurogenetics to Predict Complex and Multidimensional Autistic PhenotypesR01HD108790 · NICHD · JOHNS HOPKINS UNIVERSITY · PI Archana Venkataraman · 2022 to 2026
$1.9M
NIBIB NIH HHS R01 EB029977NICHD NIH HHS R01 HD108790NIMH NIH HHS R01 MH100028
6 · The paper itself

Abstract

Autism Spectrum Disorder standardized behavioral assessments provide quantitative measures of symptoms, yet their reliability and consistency have not been systematically evaluated. We present the first large-scale comparative analysis of four widely used assessments. We analyzed behavioral assessments across three autism cohorts using correlations, clustering, and diagnostic agreement analyses. We related behavioral variation to genetic and imaging data to evaluate biomarker associations. Sentence-level embeddings generated by large language models reveal substantial semantic overlap across instruments. Nonetheless, behavioral scores are weakly correlated (0.26 ± 0.21), and diagnostic classification shows only 65-80% agreement between tests. These patterns hold across three datasets comprising N = 1 954. None of the assessments show consistent associations with widely studied MRI or genetic biomarkers. These findings expose critical inconsistencies among widely used autism assessments and underscore the need for more reliable tools to support precision phenotyping, biomarker discovery, and individualized care. Rather than diminishing the utility of behavioral assessment in autism, the inconsistencies identified here highlight a critical opportunity to refine how behavioral phenotypes are defined and operationalized.

Indexed as

Autism Spectrum DisorderBehavioral AssessmentBiomarkerLarge Language Models

Identifiers

PMID41646735
PMCPMC12870647

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.