Update on eating disorders: current perspectives on avoidant/restrictive food intake disorder in children and youth
Bibliographic record
Abstract
Mark L Norris,1 Wendy J Spettigue,2 Debra K Katzman3 1Division of Adolescent Medicine, Department of Pediatrics, Children’s Hospital of Eastern Ontario, University of Ottawa, Ottawa, ON, Canada; 2Department of Psychiatry, Children’s Hospital of Eastern Ontario, University of Ottawa, Ottawa, ON, Canada; 3Division of Adolescent Medicine, Department of Pediatrics, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada Abstract: Avoidant/restrictive food intake disorder (ARFID) is a new eating disorder diagnosis that was introduced in the Diagnostic and Statistical Manual of Mental Disorders (DSM) fifth edition. The fourth edition of the DSM had failed to adequately capture a cohort of children, adolescents, and adults who are unable to meet appropriate nutritional and/or energy needs, for reasons other than drive for thinness, leading to significant medical and/or psychological sequelae. With the introduction of ARFID, researchers are now starting to better understand the presentation, clinical characteristics, and complexities of this disorder. This article outlines the diagnostic criteria for ARFID with specific focus on children and youth. A case example of a patient with ARFID, factors that differentiate ARFID from picky eating, and the estimated prevalence in pediatric populations are discussed, as well as clinical and treatment challenges that impact health care providers providing treatment for patients. Keywords: avoidant/restrictive food intake disorder, ARFID, eating disorder, picky eating, prevalence, treatment
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".