Longitudinal patterns of repetitive behavior in toddlers with autism
Bibliographic record
Abstract
BACKGROUND: Recent evidence suggests that restricted and repetitive behaviors may differentiate children who develop autism spectrum disorder (ASD) by late infancy. How these core symptoms manifest early in life, particularly among infants at high risk for the disorder, is not well characterized. METHODS: Prospective, longitudinal parent-report data (Repetitive Behavior Scales-Revised) were collected for 190 high-risk toddlers and 60 low-risk controls from 12 to 24 months of age. Forty-one high-risk children were classified with ASD at age 2. Profiles of repetitive behavior were compared between groups using generalized estimating equations. RESULTS: Longitudinal profiles for children diagnosed with ASD differed significantly from high- and low-risk children without the disorder on all measures of repetitive behavior. High-risk toddlers without ASD were intermediate to low risk and ASD positive counterparts. Toddlers with ASD showed significantly higher rates of repetitive behavior across subtypes at the 12-month time point. Repetitive behaviors were significantly correlated with adaptive behavior and socialization scores among children with ASD at 24 months of age, but were largely unrelated to measures of general cognitive ability. CONCLUSIONS: These findings suggest that as early as 12 months of age, a broad range of repetitive behaviors are highly elevated in children who go on to develop ASD. While some degree of repetitive behavior is elemental to typical early development, the extent of these behaviors among children who develop ASD appears highly atypical.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".