Children with avoidant/restrictive food intake disorder and anorexia nervosa in a tertiary care pediatric eating disorder program: A comparative study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to examine the medical and psychological characteristics of children under the age of 13 years with avoidant restrictive food intake disorder (ARFID) and anorexia nervosa (AN) from a Canadian tertiary care pediatric eating disorders program. METHOD: Participants included 106 children assessed between 2013 and 2017 using the Diagnostic and Statistical Manual for Mental Disorders, 5th edition (DSM-5). Data were collected through clinical interviews, psychometric questionnaires, and chart review. Information collected included medical variables (e.g., weight, heart rate, need for inpatient admission, and duration of illness from symptom onset); medical comorbidities (e.g., history of food allergies, infection, and abdominal pain preceding the eating disorder); and psychological variables (e.g., psychiatric comorbidity, self-reported depression and anxiety, and eating disorder related behaviors and cognitions). RESULTS: Children with ARFID had a longer length of illness, while those with AN had lower heart rates and were more likely to be admitted as inpatients. Children with ARFID had a history of abdominal pain and infections preceding their diagnoses and were more likely to be diagnosed with a comorbid anxiety disorder. Children with AN had a higher drive for thinness, lower self-esteem, and scored higher on depression. DISCUSSION: This is the first study to look at DSM-5 diagnosis at assessment and include psychometric and interview data with younger children with AN and ARFID. Understanding the medical and psychological profiles of children with AN and ARFID can result in a more timely and accurate diagnosis of eating disorders in younger children.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".