Classification of Children and Adolescents With Avoidant/Restrictive Food Intake Disorder
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Evidence suggests that children and adolescents with avoidant/restrictive food intake disorder (ARFID) have heterogeneous clinical presentations. To use latent class analysis (LCA) and determine the frequency of various classes in pediatric patients with ARFID drawn from a 2-year surveillance study. METHODS: Cases were ascertained using the Canadian Pediatric Surveillance Program methodology from January 1, 2016, to December 31, 2017. An exploratory LCA was undertaken with latent class models ranging from 1 to 5 classes. RESULTS: Based on fit statistics and class interpretability, a 3-class model had the best fit: Acute Medical (AM), Lack of Appetite (LOA), and Sensory (S). The probability of being classified as AM, LOA, and S was 52%, 40.7%, and 6.9%, respectively. The AM class was distinct for increased likelihood of weight loss (92%), a shorter length of illness (<12 months) (66%), medical hospitalization (56%), and heart rate <60 beats per minute (31%). The LOA class was distinct for failure to gain weight (97%) and faltering growth (68%). The S class was distinct for avoiding certain foods (100%) and refusing to eat because of sensory characteristics of the food (100%). Using posterior probability assignments, a mixed group AM/LOA (n = 30; 14.5%) had characteristics of both AM and LOA classes. CONCLUSIONS: This LCA suggests that ARFID is a heterogeneous diagnosis with 3 distinct classes corresponding to the 3 subtypes described in the literature: AM, LOA, and S. The AM/LOA group had a mixed clinical presentation. Clinicians need to be aware of these different ARFID presentations because clinical and treatment needs will vary.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".