Avoidant/restrictive food intake disorder and autism spectrum disorder: clinical implications for assessment and management
Bibliographic record
Abstract
AIM: We examined clinical and neurodevelopmental presentations of children with avoidant/restrictive food intake disorder (ARFID) to inform clinical assessment and management. METHOD: Five hundred and thirty-six patients (mean age 6y 10mo, SD 3y 5mo, range 10mo-20y; 401 males, 135 females) seen by the tertiary multidisciplinary feeding service at the Evelina London Children's Hospital between January 2013 and June 2019 were included in this case-control study. These children experienced significant feeding difficulties impacting nutrition, development, and psychosocial functioning requiring tertiary specialized input. Data on ARFID diagnosis, demographics, comorbidity, and nutrition was extracted from electronic patient records. RESULTS: Forty-nine per cent of children met ARFID criteria. The remaining participants had other difficulties including feeding, medical, and/or neurodevelopmental conditions. ARFID is more prevalent among younger patients (4-9 years) and in children with comorbid autism spectrum disorder (ASD). Younger age, comorbid ASD, and male sex significantly predicted ARFID. Diet range and male sex significantly predicted nutritional inadequacy, while comorbid ASD did not. A trend was seen between younger age and nutritional inadequacy. INTERPRETATION: Young children with ARFID should raise suspicion for ASD. Although significant nutritional deficiencies are common in children with comorbid ARFID and ASD, they are correctable with nutritional supplementation. Specialty perspective potentially limits generalizability of findings to community feeding services. We also emphasize the importance of early identification of nutritional deficits and management.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".