The prevalence and association between avoidant/restrictive food intake disorder-(ARFID) and disorders of gut–brain interaction (DGBI): a scoping review
Bibliographic record
Abstract
Avoidant/restrictive food intake disorder (ARFID) is characterized by restrictive and avoidant feeding and eating behaviors not linked to body weight or shape concerns, potentially exacerbated by disorders of gut-brain interaction (DGBI). This scoping review was conducted to systematically map the literature on ARFID and DGBI to determine the prevalence of ARFID in DGBI, the prevalence of DGBI in ARFID patients, and the association between these disorders. Online databases, including PubMed, Scopus, and Web of Science were systematically reviewed from 2013 to April 2025. Studies that reported the prevalence of ARFID in DGBI groups, the prevalence of DGBI in ARFID individuals, and examined the association between ARFID and DGBI were included. Out of 4,085 screened sources, nine studies met the inclusion criteria. The prevalence of ARFID in patients with DGBI ranged from 13.2% to 40%. Individuals with ARFID showed a higher risk for DGBI and its symptoms compared to controls. This review summarized the prevalence of ARFID in DGBI patients, and the association between these disorders. Limitations include small sample sizes and inconsistencies in diagnostic scales. Larger-scale research is needed to clarify the association, along with improved assessment tools for accurate diagnosis of ARFID and DGBI.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".