Clinical characteristics, treatment course and outcome of adults treated for avoidant/restrictive food intake disorder (ARFID) at a tertiary care eating disorders program
Bibliographic record
Abstract
BACKGROUND: Avoidant/restrictive food intake disorder (ARFID) is now recognized as a feeding/eating disorder that affects individuals across the lifespan, but research on ARFID in general and particularly in adults remains limited. The purpose of this study was to describe the demographic and clinical characteristics of adults with ARFID seeking treatment at a tertiary care eating disorders program, and to describe the course and outcomes of treatment at three levels of care-inpatient, intensive outpatient, and outpatient individual therapy. METHOD: This retrospective chart review study examined the charts of 42 patients who received treatment for ARFID between April 2020 and March 2023. Following diagnostic assessment, patients were referred to either inpatient treatment, intensive outpatient treatment, or outpatient individual therapy. All three levels of care involved individual cognitive behaviour therapy. Inpatients typically transitioned to one of the outpatient treatments as part of a continuous care plan. We examined demographic and clinical characteristics, treatment length and completion, and changes in key indicators during treatment. RESULTS: Patients were diverse with respect to demographics (e.g., 62% cisgender women; 21% cisgender men; 17% transgender, non-binary, or other gender) and comorbid concerns (e.g., 43% had neurodevelopmental disorders; > 50% had mood and anxiety disorders; 40% had posttraumatic stress disorder [PTSD]; 35% had medical conditions impacting eating/digestion). Most patients presented with more than one ARFID maintaining mechanism (i.e., lack of appetite/interest, sensory sensitivities, and/or fear of aversive consequences of eating). Treatment completion rates and outcomes were good. On average, patients showed significant improvement in impairment related to their eating disorder, and those who were underweight significantly improved on BMI and were not underweight at end of treatment. DISCUSSION: These findings add to the literature by indicating that ARFID patients are commonly male or have diverse gender identities, and have high rates of neurodevelopmental, mood, anxiety, and gastrointestinal disorders. We also found high rates of PTSD. The findings show promise for treatment outcomes across the continuum of care. Next steps in ARFID treatment and research include incorporating ARFID-specific assessments into routine care, and ongoing research investigating the efficacy and effectiveness of treatments such as CBT-AR.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".