Exploring avoidant/restrictive food intake disorder in eating disordered patients: A descriptive study
Bibliographic record
Abstract
OBJECTIVE: To assess and compare clinical characteristics of patients with avoidant/restrictive food intake disorder (ARFID) to those with anorexia nervosa (AN). METHOD: A retrospective review of adolescent eating disorder (ED) patients assessed between 2000 and 2011 that qualified for a diagnosis of ARFID was completed. A matched AN sample was used to compare characteristics between groups. RESULTS: Two hundred and five patients met inclusion criteria and were reviewed in detail. Of these, 34 (5%) patients met criteria for ARFID. A matched sample of 36 patients with AN was used to draw comparisons. Patients with ARFID were younger than those with AN, more likely to present before age 12, and more likely to be male. Patients in both groups presented at low weights. Common eating-specific behaviors and symptoms in the ARFID group included food avoidance, loss of appetite, abdominal pain, and fear of vomiting. Rates of comorbid psychiatric diagnoses and medical morbidity were high in both groups. Almost 80% of AN patients and one-third of ARFID patients required hospital admission as a result of medical instability. Symptom profiles in 4/34 ARFID patients resulted in eventual reclassification to AN. DISCUSSION: This study supports the notion that a small percentage of adolescent patients presenting with restrictive eating disorders meet criteria for ARFID. Patients are younger than average, more likely to be male compared to adolescent AN samples, and have high rates of psychiatric and medical morbidity. The study also suggests that a proportion of patients evolve into AN as treatment progresses.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".