Developing a classification system of social communication functioning of preschool children with autism spectrum disorder
Bibliographic record
Abstract
AIM: Impairments in social communication are the hallmark of autism spectrum disorder (ASD). Operationalizing 'severity' in ASD has been challenging; thus, stratifying by functioning has not been possible. The purpose of this study is to describe the development of the Autism Classification System of Functioning: Social Communication (ACSF:SC) and to evaluate its consistency within and between parent and professional ratings. METHOD: (1) ACSF:SC development based on focus groups and surveys involving parents, educators, and clinicians familiar with preschoolers with ASD; and (2) evaluation of the intra- and interrater agreement of the ACSF:SC using weighted kappa (кw ). RESULTS: Seventy-six participants were involved in the development process. Core characteristics of social communication were ascertained: communicative intent; communicative skills and reciprocity; and impact of environment. Five ACSF:SC levels were created and content-validated across participants. Best capacity and typical performance agreement ratings varied as follows: intrarater agreement on 41 children was кw =0.61 to 0.69 for parents, and кw =0.71 to 0.95 for professionals; interrater agreement between professionals was кw =0.47 to 0.61, and between parents and professionals was кw =0.33 to 0.53. INTERPRETATION: Perspectives from parents and professionals informed ACSF:SC development, providing common descriptions of the levels of everyday communicative abilities of children with ASD to complement the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. Rater agreement demonstrates that the ACSF:SC can be used with acceptable consistency compared with other functional classification systems.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".