Therapy and Psychotropic Medication Use in Young Children With Autism Spectrum Disorder
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Guidelines suggest young children with autism spectrum disorder (ASD) receive intensive nonpharmacologic interventions. Additionally, associated symptoms may be treated with psychotropic medications. Actual intervention use by young children has not been well characterized. Our aim in this study was to describe interventions received by young children (3-6 years old) with ASD. The association with sociodemographic factors was also explored. METHODS: Data were analyzed from the Autism Speaks Autism Treatment Network (AS-ATN), a research registry of children with ASD from 17 sites in the United States and Canada. AS-ATN participants receive a diagnostic evaluation and treatment recommendations. Parents report intervention use at follow-up visits. At follow-up, 805 participants had data available about therapies received, and 613 had data available about medications received. RESULTS: The median total hours per week of therapy was 5.5 hours (interquartile range 2.0-15.0), and only 33.4% of participants were reported to be getting behaviorally based therapies. A univariate analysis and a multiple regression model predicting total therapy time showed that a diagnosis of ASD before enrollment in the AS-ATN was a significant predictor. Additionally, 16.3% of participants were on ≥1 psychotropic medication. A univariate analysis and a multiple logistic model predicting psychotropic medication use showed site region as a significant predictor. CONCLUSIONS: Relatively few young children with ASD are receiving behavioral therapies or total therapy hours at the recommended intensity. There is regional variability in psychotropic medication use. Further research is needed to improve access to evidence-based treatments for young children with ASD.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".