Building evidence for the use of descriptive subtypes in youth with avoidant restrictive food intake disorder
Bibliographic record
Abstract
OBJECTIVE: To examine characteristics of patients with Avoidant/Restrictive Food Intake Disorder (ARFID) in an effort to identify and describe subtypes of the disorder. METHOD: A retrospective chart review was completed for patients aged 8-17 years assessed over a 17-year period. RESULTS: Seventy-seven patients were included in the study, the majority of whom were female (n = 56, 73%). The average age of patients was 13.7 years (SD = 2.4 years). Three specific subtypes of ARFID (aligning with example presentations outlined in the DSM-5) were identified: (a) those with weight loss and/or medical compromise as a consequence of apparent lack of interest in eating (n = 30, 39%); (b) restriction arising as a result of sensory sensitivity (n = 14, 18%); and (c) restriction based upon food avoidance and/or fear of aversive consequences of eating (n = 33, 43%). Clinical characteristics of patients varied depending on the assigned subtype. DISCUSSION: Our findings highlight the need for further research into the relative merit of subtype-assignment in patients with ARFID and whether such practice would aid in the recommended treatment. Further research is required to understand whether these categories are generalizable and applicable to other samples such as young children or adults with ARFID, and how treatment options might differ according to subtype.
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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 | 0.026 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".