ArticleAutism : the international journal of research and practice2025
Pre-trained artificial intelligence language model represents pragmatic language variability central to autism and genetically related phenotypes.
Article in Autism : the international journal of research and practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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Who cites it
1 citing paper in PubMed.
- A multi-method phenotypic study of sex differences in pragmatic language in autism.Frontiers in psychiatry · 2026Article
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Authors and funding
7 authors.
Funding
Abstract
Many individuals with autism experience challenges using language in social contexts (i.e., pragmatic language). Characterizing and understanding pragmatic variability is important to inform intervention strategies and the etiology of communication challenges in autism; however, current manual coding-based methods are often time and labor intensive, and not readily applied in ample sample sizes. This proof-of-concept methodological study employed an artificial intelligence pre-trained language model, Bidirectional Encoder Representations from Transformers, as a tool to address such challenges. We applied Bidirectional Encoder Representations from Transformers to computationally index pragmatic-related variability in autism and in genetically related phenotypes displaying pragmatic differences, namely, in parents of autistic individuals, fragile X syndrome, and
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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 Socratic graph.