Use of Psychotropic Medication in Children and Adolescents With Autism Spectrum Disorders
Bibliographic record
Abstract
OBJECTIVES: The goal of this study was to examine rates of psychotropic medication use and identify associated child and family characteristics among children and adolescents with autism spectrum disorder (ASD) enrolled in an autism registry maintained by the Autism Treatment Network (ATN). METHODS: The sample, derived from the ATN registry, consists of 2853 children aged 2 to 17 years with diagnoses of ASD supported by Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, and the Autism Diagnostic Observation Schedule with available data on medication use. As part of initial enrollment in the registry, parents completed questionnaires on current psychotropic medication use, psychiatric and medical conditions, and demographics. RESULTS: Of the 2853 children, 763 (27%) were taking ≥ 1 psychotropic medication; 15% were prescribed 1 medication, 7.4% received 2 medications, and 4.5% received ≥ 3. Among children aged 3 to 5 years, 11% were taking ≥ 1 psychotropic medication; among 6- to 11-year-old children, 46%; and 66% of adolescents aged 12 to 17 years were taking at ≥ 1 psychotropic medication. A parent report of comorbid diagnosis of attention-deficit/hyperactivity disorder, bipolar disorder, obsessive-compulsive disorder, depression, or anxiety was associated with a high rate of use, with 80% receiving ≥ 1 psychotropic medication. Only 15% of children with no comorbid psychiatric disorder were taking psychotropic medication. Psychotropic medication use was also related to sleep and gastrointestinal problems. CONCLUSIONS: The prescription of psychotropic medications in this registry sample is highly related to comorbid psychiatric disorder. Other factors associated with use include medical comorbidities, race, ethnicity, and older age.
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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 | 0.002 |
| 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 | 0.000 |
| 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".