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Record W2969389783 · doi:10.1111/jcpp.13117

Predictors of longer‐term development of expressive language in two independent longitudinal cohorts of language‐delayed preschoolers with Autism Spectrum Disorder

2019· article· en· W2969389783 on OpenAlexafffund
Vanessa H. Bal, Megan Fok, Catherine Lord, Isabel M. Smith, Pat Mirenda, Péter Szatmári, Tracy Vaillancourt, Joanne Volden, Charlotte Waddell, Lonnie Zwaigenbaum, Terry Bennett, Eric Duku, Mayada Elsabbagh, Stelios Georgiades, Wendy J. Ungar, Anat Zaidman‐Zait

Bibliographic record

VenueJournal of Child Psychology and Psychiatry · 2019
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsMcMaster UniversityHospital for Sick ChildrenGlenrose Rehabilitation HospitalUniversity of AlbertaMcGill UniversitySimon Fraser UniversityUniversity of OttawaSickKids FoundationDalhousie UniversityUniversity of TorontoCentre for Addiction and Mental HealthUniversity of British ColumbiaIzaak Walton Killam Health Centre
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Mental HealthCanadian Institutes of Health ResearchSinneave Family FoundationAutism Speaks
KeywordsAutism Diagnostic Observation SchedulePsychologyAutismJoint attentionDevelopmental psychologyAutism spectrum disorderLongitudinal studyLanguage developmentMotor skillCognitionEarly childhoodAudiologyMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Studies estimate that 30% of individuals with autism are minimally verbal. Understanding what factors predict longer-term expressive development in children with language delays is critical to inform identification and treatment of those at-risk for persistent language impairments. The present study examined predictors of expressive language development in language-delayed preschoolers followed through later school-age and young adulthood. METHODS: Children using single words or less on the Autism Diagnostic Observation Schedule (ADOS) at approximately 3 years old were drawn from the Early Diagnosis (EDX) and Pathways in ASD longitudinal cohorts. Age-3 predictors of Age-19 ADOS language level were identified using Classification and Regression Trees (CART) in the EDX sample. Linear mixed models examined the effects of CART-identified predictors on Vineland expressive communication (VExp) trajectories from Age-3 to Age-19. The same linear mixed models were examined in the Pathways sample, identifying predictors of VExp from ages 3 to 10.5 years. RESULTS: Significantly delayed fine motor skills (T-score < 20) was the strongest CART predictor of Age-19 language. In the linear mixed models, time, Age-3 fine motor skills and initiation of joint attention (IJA) predicted VExp trajectories in the EDX sample, even when controlling for Age-3 visual receptive abilities. In the Pathways sample, time and Age-3 fine motor skills were significant predictors of VExp trajectories; IJA and cognitive skills were not significant predictors. CONCLUSIONS: Marked deficits in fine motor skills may be a salient proxy marker for identifying language-delayed children with ASD who are at risk for persistent language impairments. This finding adds to the literature demonstrating a relation between motor and language development in ASD. Investigating individual skill areas (e.g., fine motor and nonverbal problem-solving skills), rather than broader indices of developmental level (e.g., nonverbal IQ) may provide important cues to understanding longer-term language outcomes that can be targeted in early intervention.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.299
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations73
Published2019
Admission routes2
Has abstractyes

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