Do reciprocal associations exist between social and language pathways in preschoolers with autism spectrum disorders?
Bibliographic record
Abstract
BACKGROUND: Differences in how developmental pathways interact dynamically in children with autism spectrum disorder (ASD) likely contribute in important ways to phenotypic heterogeneity. This study aimed to model longitudinal reciprocal associations between social competence (SOC) and language (LANG) pathways in young children with ASD. METHODS: Data were obtained from 365 participants aged 2-4 years who had recently been diagnosed with an ASD and who were followed over three time points: baseline (time of diagnosis), 6- and 12 months later. Using structural equation modeling, a cross-lagged reciprocal effects model was developed that incorporated auto-regressive (stability) paths for SOC (using the Socialization subscale of the Vineland Adaptive Behavior Scales-2) and LANG (using the Preschool Language Scale-4 Auditory Comprehension subscale). Cross-domain associations included within-time correlations and lagged associations. RESULTS: SOC and LANG were highly stable over 12 months. Small reciprocal cross-lagged associations were found across most time points and within-time correlations decreased over time. There were no differences in strength of cross-lagged associations between SOC-LANG and LANG-SOC across time points. Few differences were found between subgroups of children with ASD with and without cognitive impairment. CONCLUSIONS: Longitudinal reciprocal cross-domain associations between social competence and language were small in this sample of young children with ASD. Instead, a pattern emerged to suggest that the two domains were strongly associated around time of diagnosis in preschoolers with ASD, and then appeared to become more independent over the ensuing 12 months.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".