Health Preferences in Childhood Autism Spectrum Disorder (ASD): A Discrete Choice Experiment using the Childhood Autism Rating Scale (CARS2)
Bibliographic record
Abstract
Abstract BackgroundIn childhood autism spectrum disorder (ASD), the Childhood Autism Rating Scale–2nd edition (CARS2) instrument is used for diagnosis and assessment of severity and change. Following a feasibility study, we conducted 2 discrete choice experiments (DCEs), each of which used caregivers and clinicians as proxies for autistic children, to assess preferences for CARS2-based attributes. MethodsCaregivers and clinicians from 5 European countries received an online DCE corresponding to either the standard or the high-functioning version of the CARS2. Participants completed 14 choice tasks with 2 hypothetical profiles composed of 13 attributes set at 4 varying levels. To reduce task complexity, the 2 profiles of each choice task had at least overlap in 7 attributes, i.e., attributes were set at the same level, and presented in a stacked layout. Multinomial, mixed and scale-adjusted logit models were used to estimate preference weights. Explorative anchoring to the EQ-5D-Y was undertaken and a rescaled set of DCE coefficients is provided. ResultsModels were fit separately for caregivers and clinicians in each experiment. The final models included 563 caregivers and 666 clinicians for the standard experiment, and 346 caregivers and 310 clinicians for the high-functioning one. Caregivers and clinicians, as expected, prioritized some of the same attributes but not all. For example, in the standard experiment, Verbal communication, Non-verbal communication, and Activity-level were highly important attributes to both groups, whilst Taste, smell, and touch response and use was more important to caregivers than it was to clinicians. In the high-functioning experiment, preferences for caregivers were highest for Thinking/cognitive integration skills, Verbal communication and Fear or anxiety. For clinicians, the most important attributes were Thinking/cognitive integration skills, Fear or anxiety and Social-emotional understanding. ConclusionCaregiver and clinician preferences indicated some disparity in what constitutes the greatest unmet need for autistic children. These findings can support clinicians and caregivers in creating mutual understanding and agreement on therapeutic goals, which can be very direct and near (e.g., Taste, smell, and touch response and use) or future-oriented and developmental (e.g., Relating to people).
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".