Psychotropic Medication Use Among Medicaid-Enrolled Children With Autism Spectrum Disorders
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to provide national estimates of psychotropic medication use among Medicaid-enrolled children with autism spectrum disorders and to examine child and health system characteristics associated with psychotropic medication use. METHODS: This cross-sectional study used Medicaid claims for calendar year 2001 from all 50 states and Washington, DC, to examine 60,641 children with an autism spectrum disorder diagnosis. Logistic regression with random effects was used to examine the child, county, and state factors associated with psychotropic medication use. RESULTS: Of the sample, 56% used at least 1 psychotropic medication, 20% of whom were prescribed > or = 3 medications concurrently. Use was common even in children aged 0 to 2 years (18%) and 3 to 5 years (32%). Neuroleptic drugs were the most common psychotropic class (31%), followed by antidepressants (25%) and stimulants (22%). In adjusted analyses, male, older, and white children; those who were in foster care or in the Medicaid disability category; those who received additional psychiatric diagnoses; and those who used more autism spectrum disorder services were more likely to have used psychotropic drugs. Children who had a diagnosis of autistic disorder or who lived in counties with a lower percentage of white residents or greater urban density were less likely to use such medications. CONCLUSIONS: Psychotropic medication use is common among even very young children with autism spectrum disorders. Factors unrelated to clinical presentation seem highly associated with prescribing practices. Given the limited evidence base, there is an urgent need to assess the risks, benefits, and costs of medication use and understand the local and national policies that affect medication use.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it