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Record W4210575448 · doi:10.21203/rs.3.rs-1294995/v1

Feasibility Assessment of a Health Preference Study in Autism Based on the Childhood Autism Rating Scale (CARS2): A Qualitative Study with Clinicians and Caregivers

2022· preprint· en· W4210575448 on OpenAlexaff
Kristina Hartl, Nicholas Durno, R. Schmid, Marieke Heisen, Olivier Ethgen, Zsuzsanna Szilvasy, Evelyne Friedel, Olivier Wong, Tony Charman, Antonia San José Cáceres, Axel Mühlbacher, Ben van Hout, John Brazier, Elly Stolk

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsAutism Canada
Fundersnot available
KeywordsAutismChildhood Autism Rating ScalePreferenceComprehensionRating scaleTask (project management)PsychologyAutism spectrum disorderScale (ratio)Clinical psychologyRelevance (law)Applied psychologyDevelopmental psychologyMedicineComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.065
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.343

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0650.070
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0060.006
Scholarly communication0.0030.004
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.610
GPT teacher head0.587
Teacher spread0.023 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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