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TRUVACE RECORD VERSION
record: TRV-2026-1218
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-30T06:55:47.993267Z
status: published
lens: g_space
sector: health
headline: Exploring the Potential of Parent Report for Autism/Developmental Screening at the 18-Month Visit
dek: Purpose To develop optimal algorithms of parent-administered items to improve the detection of autism and other developmental disorders at the 18-month visit. Methods Parents of 11,878 children aged 16-20 months completed the M-CHAT-R/F ™ , Q-CHAT-10-O, and ASQ-3 R at scheduled 18-month pediatric visits via an online system. Ninety-six children with positive screens and 314 matched controls completed additional items, including the POSI; items from the FYI and POEM data banks, and the MacArthur-Bates Communicati…
gain_title: Parent-report ML model combining expressive vocabulary and joint attention items achieved over 0.7 sensitivity and specificity for both autism and developmental delay at 18-month visits, approximately doubling sensitivity versus existing screens.
problem_title: (none)
trace_subject: (none)
gain_reading: Parent-report ML model combining expressive vocabulary and joint attention items achieved over 0.7 sensitivity and specificity for both autism and developmental delay at 18-month visits, approximately doubling sensitivity versus existing screens.
gain_evidence: approximately twice as sensitive to autism as the M-CHAT-R-F and twice as sensitive as the ASQ-3 for DD | includes expressive vocabulary and items representing joint attention | generally recommended performance of over .7 for both sensitivity and specificity for both autism and DD
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers developed and tested a parent-report screening algorithm called TADAS for the 18-month pediatric visit. Using data from 11,878 toddlers who completed M-CHAT-R/F, Q-CHAT-10-O and ASQ-3, plus extended assessments in a subsample, they trained gradient-boosting models on synthetic data and evaluated them against ADOS-2 and Mullen diagnoses in an authentic holdback sample.

The resulting model, based on expressive vocabulary and joint attention items, was reported as approximately twice as sensitive as M-CHAT-R/F for autism and twice as sensitive as ASQ-3 for developmental delay, reaching over 0.7 sensitivity and specificity for both conditions. This suggests a single parent-report tool could improve early detection, but performance rests on synthetic-data training and a small validation sample of 202 children, leaving real-world generalizability uncertain.
limitation: Model training relied heavily on synthetic data generated from a small authentic subset and was evaluated on a limited holdback sample, constraining generalizability.
tag: Evidence-backed gain
key_points: Parents of 11,878 children aged 16-20 months completed M-CHAT-R/F, Q-CHAT-10-O, and ASQ-3 at scheduled 18-month pediatric visits via an online system. | 96 children with positive screens and 314 matched controls completed additional items including POSI, FYI and POEM banks, and MacArthur-Bates MCDI short-form vocabulary. | Diagnostic testing used ADOS-2 Toddler Module and Mullen; ML models used tree-based gradient boosting with Boruta feature selection via Shapley values and Bayesian hyperparameter optimization.
rundown: The study collected parent-administered items through an online system at routine 18-month visits, then enriched data with 96 screen-positive children and 314 matched controls who completed POSI and vocabulary measures. Reference standard was ADOS-2 Toddler Module and Mullen.

Training used gradient boosting trees with Boruta-Shapley feature selection and Bayesian optimization on synthetic data derived from 201 authentic cases, expanded to 25,000 training and 12,500 validation cases, with final testing on 202 authentic holdback cases.
sources:
- peer_reviewed | Journal of Autism and Developmental Disorders | https://doi.org/10.1007/s10803-026-07472-4 | 2026-09-28
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