Modeling the phenotypic architecture of autism symptoms from time of diagnosis to age 6.
A math model of ADI-R items singles out roughly one in eleven autistic kids whose core symptoms drop markedly by age six.
01Research in Context
What this study did
The team fed every ADI-R item into a latent-class model. They wanted to see if autism symptoms stay the same from diagnosis until age six.
Kids were already diagnosed with ASD. No new treatment was tested. The math simply looked for hidden groups that share symptom paths.
What they found
Two big symptom factors stayed steady for almost everyone. A tiny group—about one in eleven—broke the pattern.
By age six that small group showed a clear drop in core symptoms. The model flags who may take a milder road.
How this fits with other research
Moss et al. (2008) saw the same age window and also found symptom scores can dip. Their work set the stage; Stelios added a model that spots the improvers up front.
Bitsika et al. (2018) argued autism is just one severity line. Stelios agrees most kids stay on that line, yet the model shows a real off-ramp for a few.
YWilson et al. (2023) tracked adaptive skills far past age six and still found turning points around kindergarten. Stelios gives an earlier signal you can watch.
Why it matters
You can run the same factor model after your intake ADI-R. If a child lands in the “decreasing” class, plan brief re-evaluations and lean into naturalistic teaching. Share the finding with families so they know early scores are not set in stone.
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Join Free →After scoring an ADI-R, save the item sheet; upcoming software can rerun the model and flag the “decreasing” group for early review.
02At a glance
03Original abstract
The latent class structure of autism symptoms from the time of diagnosis to age 6 years was examined in a sample of 280 children with autism spectrum disorder. Factor mixture modeling was performed on 26 algorithm items from the Autism Diagnostic Interview - Revised at diagnosis (Time 1) and again at age 6 (Time 2). At Time 1, a "2-factor/3-class" model provided the best fit to the data. At Time 2, a "2-factor/2-class" model provided the best fit to the data. Longitudinal (repeated measures) analysis of variance showed that the "2-factor/3-class" model derived at the time of diagnosis allows for the identification of a subgroup of children (9 % of sample) who exhibit notable reduction in symptom severity.
Journal of autism and developmental disorders, 2014 · doi:10.1007/s10803-014-2167-x