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dc.creatorDominique J. Monlezun
dc.creatorDart, Lyn
dc.creatorVanbeber, Anne
dc.creatorSmith-Barbaro, Peggy
dc.creatorCostilla, Vanessa
dc.creatorSamuel, Charlotte
dc.creatorTerregino, Carol A.
dc.creatorErcikan Abali, Emine
dc.creatorDollinger, Beth
dc.creatorBaumgartner, Nicole
dc.creatorKramer, Nicholas
dc.creatorSeelochan, Alex
dc.creatorTaher, Sabira
dc.creatorDeutchman, Mark
dc.creatorEvans, Meredith
dc.creatorEllis, Robert B.
dc.creatorOyola, Sonia
dc.creatorMaker-Clark, Geeta
dc.creatorDreibelbis, Tomi
dc.creatorBudnick, Isadore
dc.creatorTran, David
dc.creatorDeValle, Nicole
dc.creatorShepard, Rachel
dc.creatorChow, Erika
dc.creatorPetrin, Christine
dc.creatorRazavi, Alexander
dc.creatorMcGowan, Casey
dc.creatorGrant, Austin
dc.creatorBird, Mackenzie
dc.creatorCarry, Connor
dc.creatorMcGowan, Glynis
dc.creatorMcCullough, Colleen
dc.creatorBerman, Casey M.
dc.creatorDotson, Kerri
dc.creatorNiu, Tianhua
dc.creatorSarris, Leah
dc.creatorHarlan, Timothy S.
dc.creatorCHOP Co-investigators
dc.description.abstractBackground. Cardiovascular disease (CVD) annually claims more lives and costs more dollars than any other disease globally amid widening health disparities, despite the known significant reductions in this burden by low cost dietary changes. The world's first medical school-based teaching kitchen therefore launched CHOP-Medical Students as the largest known multisite cohort study of hands-on cooking and nutrition education versus traditional curriculum for medical students. Methods. This analysis provides a novel integration of artificial intelligence-based machine learning (ML) with causal inference statistics. 43 ML automated algorithms were tested, with the top performer compared to triply robust propensity score-adjusted multilevel mixed effects regression panel analysis of longitudinal data. Inverse-variance weighted fixed effects meta-analysis pooled the individual estimates for competencies. Results. 3,248 unique medical trainees met study criteria from 20 medical schools nationally from August 1, 2012, to June 26, 2017, generating 4,026 completed validated surveys. ML analysis produced similar results to the causal inference statistics based on root mean squared error and accuracy. Hands-on cooking and nutrition education compared to traditional medical school curriculum significantly improved student competencies (OR 2.14, 95% CI 2.00-2.28, ) and MedDiet adherence (OR 1.40, 95% CI 1.07-1.84, ), while reducing trainees' soft drink consumption (OR 0.56, 95% CI 0.37-0.85, ). Overall improved competencies were demonstrated from the initial study site through the scale-up of the intervention to 10 sites nationally (). Discussion. This study provides the first machine learning-augmented causal inference analysis of a multisite cohort showing hands-on cooking and nutrition education for medical trainees improves their competencies counseling patients on nutrition, while improving students' own diets. This study suggests that the public health and medical sectors can unite population health management and precision medicine for a sustainable model of next-generation health systems providing effective, equitable, accessible care beginning with reversing the CVD epidemic.en_US
dc.sourceBioMed Research International
dc.subjectRandomized controlled trialen_US
dc.subjectMediterranean dieten_US
dc.subjectcardiovascular diseaseen_US
dc.subjectcausal inferenceen_US
dc.subjectrelative risken_US
dc.titleMachine Learning-Augmented Propensity Score-Adjusted Multilevel Mixed Effects Panel Analysis of Hands-On Cooking and Nutrition Education versus Traditional Curriculum for Medical Students as Preventive Cardiology: Multisite Cohort Study of 3,248 Trainees over 5 Yearsen_US
dc.rights.holder2018 Dominique J. Monlezun et al.
dc.rights.licenseCC BY 4.0
local.collegeCollege of Science and Engineering
local.departmentNutritional Sciences
local.personsDart, Vanbeber (NTDT)

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