ArticleJournal of the American Academy of Child and Adolescent Psychiatry2021
Towards a Data-Driven Approach to Screen for Autism Risk at 12 Months of Age.
Article in Journal of the American Academy of Child and Adolescent Psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- The Vocabulary of Infants with an Elevated Likelihood and Diagnosis of Autism Spectrum Disorder: A Systematic Review and Meta-Analysis of Infant Language Studies Using the CDI and MSEL.International journal of environmental research and public health · 2022Pooled it
- Subclinical neuropsychiatric trait variation in parents of children with autism spectrum disorder: a cohort study.Molecular autism · 2026Article
- Parent-mediated early intervention in infants and toddlers at elevated likelihood for autism: a systematic review of randomized controlled trials.Frontiers in psychiatry · 2026Review
- Developmental Impacts of Early Sensory Patterns on School-Age Adaptive, Maladaptive, and Participation Outcomes in Autistic and Non-autistic Children.Journal of autism and developmental disorders · 2025Article
- Links Between Early Prelinguistic Communication and Later Expressive Language in Toddlers With Autistic and Non-Autistic Siblings.Journal of speech, language, and hearing research : JSLHR · 2025Article
- Prediction of autism in infants: progress and challenges.The Lancet. Neurology · 2023Review
- Associations Between Caregiver Stress and Language Outcomes in Infants With Autistic and Non-Autistic Siblings: An Exploratory Study.Journal of speech, language, and hearing research : JSLHR · 2023Article
- Hypoconnectivity between anterior insula and amygdala associates with future vulnerabilities in social development in a neurodiverse sample of neonates.Scientific reports · 2022Article
- Predicting Autism in Infancy.Journal of the American Academy of Child and Adolescent Psychiatry · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors at 8 institutions in 3 countries.
Funding
Abstract
objectiveThis study aimed to develop a classifier for infants at 12 months of age based on a parent-report measure (the First Year Inventory 2.0 [FYI]), for the following reasons: (1) to classify infants at elevated risk, above and beyond that attributable to familial risk status for ASD; and (2) to serve as a starting point to refine an approach for risk estimation in population samples.
methodA total of 54 high-familial risk (HR) infants later diagnosed with ASD (HR-ASD), 183 HR infants not diagnosed with ASD at 24 months of age (HR-Neg), and 72 low-risk controls participated in the study. All infants contributed FYI data at 12 months of age and had a diagnostic assessment for ASD at age 24 months. A data-driven, cross-validated analytic approach was used to develop a classifier to determine screening accuracy (eg, sensitivity) of the FYI to classify HR-ASD and HR-Neg.
resultsThe newly developed FYI classifier had an estimated sensitivity of 0.71 (95% CI: 0.50, 0.91) and specificity of 0.72 (95% CI: 0.49, 0.91).
conclusionThis classifier demonstrates the potential to improve current screening for ASD risk at 12 months of age in infants already at elevated familial risk for ASD, increasing opportunities for detection of autism risk in infancy. Findings from this study highlight the utility of combining parent-report measures with machine learning approaches.
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Registered trials
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.