Multimodal therapy for rigid, persistent avoidant/restrictive food intake disorder (ARFID) since infancy: A case report
Bibliographic record
Abstract
Avoidant/restrictive food intake disorder (ARFID) is a feeding and eating disorder that results in nutritional inadequacies, weight loss, and/or dependence on enteral feeds, and for which three clinical subtypes have been described. We present a unique case of an 11-year-old boy with rigid ARFID since infancy and features of all three ARFID subtypes. The patient presented with a life-long history of sensory aversion, limited intake and phobia of vomiting resulting in restriction to a single food item (yogurt) for more than 5 years. He presented with severe iron-deficiency anaemia, and deficiencies of vitamins A, C, D, E and zinc. We employed a multimodal therapeutic approach that incorporated elements of cognitive-behavioural therapy (CBT), family-based therapy (FBT) and pharmacological management with an antidepressant medication (sertraline) and an atypical antipsychotic agent (olanzapine). Over the course of a 7-week admission, our approach assisted the patient in successful weight restoration and incorporation of at least three new food items into his daily diet. While there are currently no first-line recommendations for ARFID management, our study lends support to the efficacy of CBT, FBT and pharmacological management for ARFID patients, including complex cases with multiple subtype features. Further research is needed to strengthen ARFID clinical guidelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".