Feasibility Assessment of a Health Preference Study in Autism Based on the Childhood Autism Rating Scale (CARS2): A Qualitative Study with Clinicians and Caregivers
Bibliographic record
Abstract
Abstract BackgroundIn childhood autism spectrum disorder (ASD), the Childhood Autism Rating Scale–2nd edition (CARS2) is a condition-specific instrument to be filled out by clinicians, resulting in a score for diagnosis and severity. Our aim is to estimate a preference-based scoring of CARS2 to better understand the value of changes in the CARS2 score. Using caregivers and clinicians as proxies for autistic children, we assessed the feasibility of establishing preferences for CARS2-based attributes.MethodsThe 15 CARS2 items were assessed regarding their relevance as attributes and the appropriateness of their wording. Best-worst scaling (BWS) and discrete choice experiment (DCE) choice task designs were developed, as well as a mapping task between CARS2 and the EQ-5D-Y. Individual qualitative interviews with caregivers, clinicians, and autistic adults were conducted (N=10). A committee of experts advised on the study, including caregivers, clinicians, and specialists in CARS2, health technology assessment, and preference research methodology. ResultsThirteen of the 15 CARS2 items were deemed appropriate attributes for the preference study. Caregivers and autistic adults identified with the attributes and perceived them as comprehensive. The attribute definitions and level descriptions were shortened and refined to ensure comprehension for non-experts. The choice tasks were challenging for participants; however, 9/10 respondents could make a choice. Interviewees favored a two-profile DCE over a three-profile BWS design. In a subsequent internal pilot (N=13), participants favored a stacked layout, in which attributes with overlapping levels were clustered. The CARS2/EQ-5D-Y mapping task was feasible using step-by-step instructions. ConclusionA preference study in childhood ASD based on the CARS2 instrument is feasible with caregivers and clinicians as proxies for children’s preferences.
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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 | 0.065 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".