The social responsiveness scale is an efficient screening tool for autism spectrum disorder traits in adults with anorexia nervosa
Bibliographic record
Abstract
OBJECTIVE: A significant proportion of individuals with anorexia nervosa (AN) show high levels of autism spectrum disorder (ASD) traits, a factor associated with poorer treatment outcomes. An important question for both researchers and clinicians relates to how ASD traits should be assessed in individuals with AN. This study aimed to examine scores on the Social Responsiveness Scale adult self-report version (SRS-2) in individuals in the acute (AN) and recovered stages (REC) of illness compared to healthy controls (HCs). We also aimed to examine associations between the SRS-2 and an observational diagnostic measure, the Autism Diagnostic Observation Schedule - second edition (ADOS-2). METHOD: The SRS-2 and ADOS-2 were administered to 142 adults with AN, REC, and HCs. Eating disorder (ED) psychopathology and functional impairment were also assessed. RESULTS: AN and REC scored significantly higher than HCs on the SRS-2. SRS-2 scores significantly predicted ADOS-2 classification and were positively associated with ED psychopathology and functional impairment. SRS-2 scores were not associated with BMI or illness duration. CONCLUSIONS: The SRS-2 may be a useful tool in screening for ASD traits in individuals with AN. Although cross-sectional, the results also suggest ASD symptoms are independent of BMI and persist in individuals recovered from AN.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".