Behavior Predictors of Language Development Over 2 Years in Children With Autism Spectrum Disorders
Bibliographic record
Abstract
PURPOSE: This exploratory study examined predictive relationships between 5 types of behaviors and the trajectories of vocabulary and language development in young children with autism over 2 years. METHOD: Participants were 69 children with autism assessed using standardized measures prior to the initiation of early intervention (T1) and 6 months (T2), 12 months (T3), and 24 months (T4) later. Growth curve modeling examined the extent to which behaviors at T1 and changes in behaviors between T1 and T2 predicted changes in development from T1 to T4. RESULTS: Regardless of T1 nonverbal IQ and autism severity, high scores for inattentive behaviors at T1 predicted lower rates of change in vocabulary production and language comprehension over 2 years. High scores for social unresponsiveness at T1 predicted lower rates of change in vocabulary comprehension and production and in language comprehension over 2 years. Scores for insistence on sameness behaviors, repetitive stereotypic motor behaviors, and acting-out behaviors at T1 did not predict the rate of change of any child measure over 2 years beyond differences accounted for by T1 autism severity and nonverbal IQ status. CONCLUSIONS: The results are discussed with regard to their implications for early intervention and understanding the complex factors that affect developmental outcomes.
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 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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".