AI Article Synopsis

  • - A new approach combines two autism screening tools, the STAT and SORF, to improve the identification process for toddlers, utilizing the strengths of both measures.
  • - The study analyzed data from a diagnostic program, where experts applied the SORF checklist to videos of children's social behaviors recorded by STAT to enhance classification accuracy.
  • - Results showed the combined STAT-SORF model achieved a 90% classification rate, suggesting that using multiple tools can help diagnose autism earlier and improve access to interventions.

Article Abstract

Purpose: A multimeasure approach was developed to capitalize on the strengths of two screening measures: the Screening Tool for Autism in Toddlers and Young Children (STAT), an observational measure of social communication, and the Systematic Observation of Red Flags (SORF), a checklist including restricted and repetitive behavior (RRB) items. This approach offers a novel method of identifying autism in toddlers.

Method: This was a retrospective study of data collected from a multidisciplinary diagnostic program for 24- to 36-month-olds with developmental delays. Raters with autism expertise but naïve to diagnoses applied the SORF to STAT videos. Psychometrics were derived for the SORF on STAT observations and a multiple-measure approach that used a Least Absolute Shrinkage and Selection Operator modeling framework to construct a STAT-SORF RRB Hybrid, retaining SORF RRB items based on individual predictive abilities.

Results: The SORF alone correctly classified 84% of the sample (84% sensitivity and 86% specificity). The STAT-SORF RRB Hybrid model, which retained four SORF RRB items, correctly classified 90% of a validation sample (95% sensitivity and 75% specificity).

Conclusion: These findings highlight the potential utility of using multiple autism identification tools and regression-based scoring to establish presumptive eligibility and facilitate early access to autism interventions.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9911122PMC
http://dx.doi.org/10.1044/2022_AJSLP-22-00132DOI Listing

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