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Record W4411697637 · doi:10.1080/10640266.2025.2519904

The prevalence and association between avoidant/restrictive food intake disorder-(ARFID) and disorders of gut–brain interaction (DGBI): a scoping review

2025· review· en· W4411697637 on OpenAlexaff
Sina Tamaskani Zahedi, Parisa Hajihashemi, Hamid Nasiri‐Dehsorkhi, Amrollah Ebrahimi, Peyman Adibi, David Armstrong

Bibliographic record

VenueEating Disorders · 2025
Typereview
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsAssociation (psychology)PsychologyEating disordersClinical psychologyFood intakePsychiatryConduct disorderMedicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.638
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.339
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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