Evidence map›Paper›PMID 41649631›Full record

ArticleResearch on child and adolescent psychopathology2026

Using Machine Learning to Identify Infant and Child Environmental and Biological Predictors of Callous-Unemotional Traits.

Nicole Huth, Sarah C Vogel, Patrick K Goh, Alexis Broussard, Emily Perkins, Yael Paz, Anthony J Rosellini, Roger Mills-Koonce, Michael T Willoughby, Rebecca Waller and 1 more

Abstract read
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In one paragraph

Article in Research on child and adolescent psychopathology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

11 authors.

Nicole HuthDepartment of Psychological and Brain Sciences, Boston University, MA, Boston, USA.ORCID 0009-0004-9905-5462
Sarah C VogelDepartment of Psychology, University of Massachusetts Boston, Boston, USA.
Patrick K GohDepartment of Psychology, University of Hawai'i at Mānoa, Honolulu, USA.
Alexis BroussardDepartment of Psychology, University of Pennsylvania, Philadelphia, USA.
Emily PerkinsDepartment of Psychology, University of Pennsylvania, Philadelphia, USA.
Yael PazDepartment of Psychology, University of Pennsylvania, Philadelphia, USA.
Anthony J RoselliniDepartment of Psychological and Brain Sciences, Boston University, MA, Boston, USA.
Roger Mills-KoonceSchool of Education, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Michael T WilloughbyRTI International, Durham, USA.
Rebecca WallerSchool of Education, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Nicholas J WagnerDepartment of Psychological and Brain Sciences, Boston University, MA, Boston, USA. njwagner@bu.edu.

Funding

Leveraging Machine Learning Techniques to Elucidate Risk for Callous-Unemotional TraitsR21MH126162 · NIMH · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI WAGNER, NICHOLAS J, WALLER, REBECCA · 2022 to 2023
$450k
Fontaine Fellowship Fontaine FellowshipIsrael Science Foundation Israel Science FoundationMindCORE Postdoctoral Fellowship MindCORE Postdoctoral FellowshipNational Science Foundation Graduate Research Fellowship Program DGE-2236662NIMH NIH HHS MH126162NIMH NIH HHS R21 MH126162
6 · The paper itself

Abstract

Callous-unemotional (CU) traits (i.e., blunted affect, low guilt) develop through the interplay of neurophysiological and environmental factors. However, critical gaps remain in understanding the relative importance of different physiological systems and early experiences over time. The goal of the current study is to identify which early-life biological and environmental features at which time points most strongly predict later CU traits in middle childhood. Using prospective longitudinal data from a rural community sample (N = 725; 48% female; 34% Black) and machine learning models, this study examined the relative predictive influence of biological stress systems (Sympathetic Nervous System (SNS), Hypothalamic-Pituitary-Adrenal (HPA) axis) and adversity indices (exposure to intimate partner violence (IPV), economic hardship, and lack of socially and cognitively stimulating toys or activities in the home) between 6 and 48 months on CU traits and conduct disorder (CD) at age 7. Models explained 7.3% of the variance in CU traits at 7 years. Exposure to IPV and sociocognitive resources across several time points, particularly in toddlerhood, emerged as influential predictors of later CU traits. Additionally, SNS functioning (i.e., salivary alpha-amylase) in early childhood was the most influential physiological predictor of CU traits. Prediction for CD was limited, with the final model explaining only 3.4% of the variability. Findings highlight the role of sympathetic regulation and early life experiences in shaping CU traits, providing important insights for the development of targeted interventions.

Indexed as

Adverse Childhood ExperiencesConduct DisorderEmotionsMachine LearningSympathetic Nervous SystemChildChild, PreschoolFemaleHumansHypothalamo-Hypophyseal SystemInfantLongitudinal StudiesMalePituitary-Adrenal SystemPredictive Learning ModelsProspective StudiesAdversityCallous-unemotional traitsConduct disorderMachine learningPhysiology

Identifiers

PMID41649631
PMCPMC13524131

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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.