An Artificial Intelligence-Assisted Umbrella Review of Eating Disorder Meta-Analyses (2020–2025): Mapping Treatment Efficacy, Comorbidity, and Persistent Gaps in Sample Diversity
Nutrients · 2026
Abstract e pubblicazione originale
L’abstract è consultabile nella fonte originale. Non viene ripubblicato qui perché non risulta verificata una licenza compatibile.
Leggi nella fonte originale ↗Parole chiave
Eating Disorders and BehaviorsNutrition, Health and Food BehaviorChild Nutrition and Feeding Issueseating disordersmeta-analysisartificial intelligencelarge language modelsrisk of bias assessmentneural topic modelingtreatment efficacypsychiatric comorbiditysample diversitygeneralizabilityavoidant/restrictive food intake disordereating disorder prevalenceCOVID-19 pandemicpharmacotherapybehavioral addictionlong-term outcomesprevention
Come citare questo studio
E. Caitlin Lloyd, Pablo del Sol Calderón, Cate Morales, Euna Lee, Sarita Maharajh, Brooke Moorman, Daniel Le Grange, Kelsey E. Hagan, Sasha Gorrell. (2026). An Artificial Intelligence-Assisted Umbrella Review of Eating Disorder Meta-Analyses (2020–2025): Mapping Treatment Efficacy, Comorbidity, and Persistent Gaps in Sample Diversity. Nutrients. DOI: 10.3390/nu18213650
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