Developmental trajectories of adaptive behavior in autism spectrum disorder: a high‐risk sibling cohort
Bibliographic record
Abstract
BACKGROUND: Children with autism spectrum disorder (ASD) often experience impairments in adaptive behavior. METHODS: Developmental trajectories of adaptive behavior in ASD were examined in children from high-risk (siblings of children diagnosed with ASD, n = 403) and low-risk (no family history of ASD, n = 163) families. Children were assessed prospectively at 12, 18, 24, and 36 months of age using the Vineland Adaptive Behavior Scales and underwent a blind independent diagnostic assessment for ASD at 36 months of age. RESULTS: The semi-parametric group-based modeling approach using standard scores on the Adaptive Behavior Composite revealed three distinct developmental trajectories: (a) Group 1 (21.2% of sample) showed average performance at 12 months and a declining trajectory; (b) Group 2 (52.8% of the sample) showed average performance at 12 months with a slightly declining trajectory; and (c) Group 3 (26.0% of the sample) showed a higher level of adaptive behavior at 12 months and a stable trajectory. The Mullen Scales of Early Learning Early Learning Composite and the Autism Observation Scale for Infants total score at 6 and 12 months predicted trajectory membership. CONCLUSIONS: The results emphasize heterogeneous development associated with ASD and the need for interventions tailored to individual presentations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".