Treatment of children and adolescents with avoidant/restrictive food intake disorder: a case series examining the feasibility of family therapy and adjunctive treatments
Bibliographic record
Abstract
BACKGROUND: To date, little research has examined the effectiveness of either modified Family-Based Therapy or psychopharmacological treatments for patients diagnosed with avoidant/restrictive food intake disorder (ARFID), and there is little evidence to guide clinicians treating children and adolescents with ARFID. This case series describes the clinical presentations, treatments and outcomes of six patient diagnosed with ARFID who were treated sequentially by a child psychiatrist and adolescent medicine physician in a hospital-based eating disorder program. CASE PRESENTATIONS: = 7.08) when family therapy began. Cases 1, 2 and 3 were admitted to a specialized inpatient unit at assessment due to medical instability (2) or failed outpatient treatment (1), and all six cases presented with severe co-morbid anxiety. All patients were treated using a combination of medical monitoring, family therapy, medication (including olanzapine, fluoxetine and in two cases cyproheptadine), and cognitive behavioural therapy. At treatment termination, all six patients had achieved their goal weight. CONCLUSION: These cases illustrate the complex ways in which young patients with ARFID can present, the illness' effect on development and mental health, and the positive outcomes associated with weight gain and concurrent treatment for co-morbid anxiety disorders.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".