Evidence map›Paper›PMID 41612415›Full record

ArticleMolecular autism2026

Sub-groups of spoken language and broader communication skills in a large heterogenous cohort of minimally verbal school-age children: evidence of discrepant profiles.

Jo Saul, Mollie Cooke, Supipi Munaweera, Danielle Matthews

Abstract read
In one paragraph

Article in Molecular autism, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jo SaulClinical, Educational and Health Psychology, University College London, 26 Bedford Way, London, WC1H 0AP, UK. jo.saul.14@ucl.ac.uk.
Mollie CookeClinical, Educational and Health Psychology, University College London, 26 Bedford Way, London, WC1H 0AP, UK.
Supipi MunaweeraClinical, Educational and Health Psychology, University College London, 26 Bedford Way, London, WC1H 0AP, UK.
Danielle MatthewsSchool of Psychology, ICOSS, University of Sheffield, 219 Portobello, Sheffield, S1 4DP, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCommunication and language profiles in neurodevelopmental conditions are characterised by enormous phenotypic heterogeneity. We sought to identify subgroups of Minimally Verbal (MV) children in a school-age transdiagnostic sample. We hypothesised that a cluster with a discrepant profile (strong receptive but low speech production and expressive spoken language skills) would emerge.

methodsWe recruited MV children and their families (n = 193; mean age 7.6 years (sd: 2.5, range 4–13); 73% male). The sample varied in their adaptive skills and range of diagnoses (autism 77%, genetic syndrome 15%). Children took part in a play-based experimenter-child interaction designed to elicit communicative acts such as requesting or sharing attention. Parents completed questionnaires about their child’s developmental profile, communicative and adaptive skills. Additional in-person batteries probed children’s motor, imitative and receptive language skills. The multi-task, multi-informant communication-related variables were then entered into a pre-registered agglomerative hierarchical cluster analysis.

resultsSix distinct clusters emerged and were compared in relation to non-social autism symptoms, motor skills, adaptive skills and demographic measures. For four clusters, children’s receptive, expressive, adaptive and motor skills were fairly commensurate and could be described as very low-, low-, mid- or high-skill. Two further clusters described discrepant profiles of ability where speech and spoken language skills were disproportionately lower. Exploratory analyses revealed that children in different clusters differed in terms of their diagnostic profiles, use of Augmentative and Alternative Communication (AAC) and echolalia. LIMITATIONS: Whilst the inclusive, trait-based, transdiagnostic approach taken has high ecological validity, some measures employed were thus necessarily bespoke, adapted or non-normed and reported diagnoses did not undergo systematic validation.

conclusionThe hypothesised discrepant profile emerged whereby some MV children had stronger receptive than expressive skills, suggesting motor barriers to speech that necessitate tailored support.

Indexed as

CommunicationSpeechChildChild, PreschoolCluster AnalysisCohort StudiesFemaleHumansMaleAutismCommunicationLanguageMinimally verbalSpeechSubgroups

Identifiers

PMID41612415
PMCPMC12874986

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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 OpenQuestion graph.