Cognitive‐behavioral therapy for avoidant/restrictive food intake disorder: Feasibility, acceptability, and proof‐of‐concept for children and adolescents
Bibliographic record
Abstract
OBJECTIVE: Little is known about the optimal treatment of avoidant/restrictive food intake disorder (ARFID). The purpose of this study was to evaluate feasibility, acceptability, and proof-of-concept for cognitive-behavioral therapy for ARFID (CBT-AR) in children and adolescents. METHOD: Males and females (ages 10-17 years) were offered 20-30 sessions of CBT-AR delivered in a family-based or individual format. RESULTS: Of 25 eligible individuals, 20 initiated treatment, including 17 completers and 3 dropouts. Using intent-to-treat analyses, clinicians rated 17 patients (85%) as "much improved" or "very much improved." ARFID severity scores (on the Pica, ARFID, and Rumination Disorder Interview) significantly decreased per both patient and parent report. Patients incorporated a mean of 16.7 (SD = 12.1) new foods from pre- to post-treatment. The underweight subgroup showed a significant weight gain of 11.5 (SD = 6.0) pounds, moving from the 10th to the 20th percentile for body mass index. At post-treatment, 70% of patients no longer met criteria for ARFID. DISCUSSION: This is the first study of an outpatient manualized psychosocial treatment for ARFID in older adolescents. Findings provide evidence of feasibility, acceptability, and proof-of-concept for CBT-AR. Randomized controlled trials are needed.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".