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Updated: Jun 21, 2026
09:16
Use of a Video Scoring Anchor for Rapid Serial Assessment of Social Communication in Toddlers
Published on: March 14, 2018
Using Real-World Evidence to Estimate the Predictive Value of Screening Questionnaires: Autism Screening as a Case
R Christopher Sheldrick1, J L Hooker2, M Davignon3
1Department of Psychiatry (RC Sheldrick), UMass Chan School of Medicine, Worcester, Mass.
Academic Pediatrics
|June 19, 2026
Summary
This study assessed autism screening tools using real-world data, finding the Parents' Observations of Social Interactions (POSI) shows good accuracy for identifying autism in young children.
Area of Science:
- Pediatric Healthcare
- Developmental Pediatrics
- Public Health
Background:
- Autism Spectrum Disorder (ASD) screening is crucial for early intervention.
- Real-world evidence is needed to validate screening tool accuracy.
- Electronic Medical Records (EMR) offer a valuable data source for such validation.
Purpose of the Study:
- To estimate the accuracy of autism screening questionnaires using real-world data.
- To determine sensitivity, specificity, and predictive probabilities for screening scores.
- To inform clinical care by understanding the predictive value of screening results.
Main Methods:
- Utilized EMR data from 33,490 children aged 4 years and under.
- Assessed Parents' Observations of Social Interactions (POSI) as a primary screener.
- Evaluated Modified Checklist for Autism in Toddlers-Revised (M-CHAT-R) as a secondary screener.
- Primary outcome was autism diagnosis by age 4 years.
Main Results:
- POSI demonstrated 69.5% sensitivity and 76.4% specificity at 18 months.
- POSI showed 70.3% sensitivity and 80.8% specificity at 24 months.
- Predictive probabilities varied significantly by POSI score, with higher scores indicating greater likelihood of diagnosis.
Conclusions:
- EMR data provide valuable insights into autism screening tool performance.
- Findings support the accuracy of the POSI for autism screening.
- Replication in diverse health systems and over time is recommended to ensure generalizability and track trends.
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