Service Use Classes Among School-aged Children From the Autism Treatment Network Registry
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Use of specific services may help to optimize health for children with autism spectrum disorder (ASD); however, little is known about their service use patterns. We aimed to (1) define service use groups and (2) determine associations of sociodemographic, developmental, behavioral, and health characteristics with service use groups among school-aged children with ASD. METHODS: We analyzed cross-sectional data on 1378 children aged 6 to 18 years with an ASD diagnosis from the Autism Speaks Autism Treatment Network registry for 2008-2015, which included 16 US sites and 2 Canadian sites. Thirteen service use indicators spanning behavioral and medical treatments (eg, developmental therapy, psychotropic medications, and special diets) were examined. Latent class analysis was used to identify groups of children with similar service use patterns. RESULTS: By using latent class analysis, school-aged children with ASD were placed into 4 service use classes: limited services (12.0%), multimodal services (36.4%), predominantly educational and/or behavioral services (42.6%), or predominantly special diets and/or natural products (9.0%). Multivariable analysis results revealed that compared with children in the educational and/or behavioral services class, those in the multimodal services class had greater ASD severity and more externalizing behavior problems, those in the limited services class were older and had less ASD severity, and those in the special diets and/or natural products class had higher income and poorer quality of life. CONCLUSIONS: In this study, we identified 4 service use groups among school-aged children with ASD that may be related to certain sociodemographic, developmental, behavioral, and health characteristics. Study findings may be used to better support providers and families in decision-making about ASD services.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".