An examination of the clinical outcomes of adolescents and young adults with broad autism spectrum traits and autism spectrum disorder and anorexia nervosa: A multi centre study
Bibliographic record
Abstract
OBJECTIVES: To compare the clinical outcomes of adolescents and young adults with anorexia nervosa (AN) comorbid with broad autism spectrum disorder (ASD) or ASD traits. METHOD: The developmental and well-being assessment and social aptitude scale were used to categorize adolescents and young adults with AN (N = 149) into those with ASD traits (N = 23), and those who also fulfilled diagnostic criteria for a possible/probable ASD (N = 6). We compared both eating disorders specific measures and broader outcome measures at intake and 12 months follow-up. RESULTS: Those with ASD traits had significantly more inpatient/day-patient service use (p = .015), as well as medication use (p < .001) at baseline. Both groups had high social difficulties and poorer global functioning (strengths and difficulties questionnaire) at baseline, which improved over time but remained higher at 12 months in the ASD traits group (p = .002). However, the improvement in eating disorder symptoms at 12 months was similar between groups with or without ASD traits. Treatment completion rates between AN only and ASD traits were similar (80.1 vs. 86.5%). DISCUSSION: Adolescents with AN and ASD traits show similar reductions in their eating disorder symptoms. Nevertheless, their social difficulties remain high suggesting that these are life-long difficulties rather than starvation effects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".