ArticleEating and weight disorders : EWD2021
An artificial intelligence-derived tool proposal to ease disordered eating screening in people with obesity.
Article in Eating and weight disorders : EWD, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02857179 (Prospective Cohort Study Monitoring Patients Undergoing Bariatric Surgery), which is not on this map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Prospective Cohort Study Monitoring Patients Undergoing Bariatric Surgery
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- A Systematic Review on Applications of Artificial Intelligence for Obesity Prevention.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026Pooled it
- The Role of Artificial Intelligence in Obesity Risk Prediction and Management: Approaches, Insights, and Recommendations.Medicina (Kaunas, Lithuania) · 2025Review
- Artificial intelligence and eating disorders: a commentary.Eating and weight disorders : EWD · 2023Article
- Artificial intelligence and obesity management: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023.Obesity pillars · 2023Article
- Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review.JMIR mental health · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeIn people with obesity, food addiction (FA) tends to be associated with poorer outcomes. Its diagnosis can be challenging in primary care. Based on the SCOFF example, we aim to determine whether a quicker and simpler screening tool for FA in people with obesity could be developed, using artificial intelligence (machine learning).
methodsThe first step was to look for the most discriminating items, among 152 different ones, to differentiate between FA-positive and FA-negative populations of patients with obesity. Items were ranked using the Fast Correlation-Based Filter (FCBF). Retained items were used to test the performance of nine different predictive algorithms. Then, the construction of a graphic tool was proposed.
resultsData were available for 176 patients. Only three items had a FCBF score > 0.1: "I eat to forget my problems"; "I eat more when I'm alone"; and "I eat sweets or comfort foods". Naive Bayes classification obtained best predictive performance. Then, we created a 3-item nomogram to predict a positive scoring on the YFAS.
conclusionA simple and fast screening tool for detecting high-disordered eating risk is proposed. The next step will be a validation study of the FAST nomogram to ensure its relevance for emotional eating diagnosis. LEVEL OF EVIDENCE: Level V, cross-sectional descriptive study. CLINICAL TRIAL REGISTRY NUMBER: NCT02857179 at clinicaltrials.gov.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.