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1
Machine 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 Years.基于机器学习的倾向评分调整多层次混合效应面板分析:医学生实践烹饪与营养教育与传统课程在预防心脏病学中的效果——5 年 3248 名学员的多站点队列研究。
Biomed Res Int. 2018 Apr 15;2018:5051289. doi: 10.1155/2018/5051289. eCollection 2018.
2
Joint mixed-effects models for causal inference with longitudinal data.具有纵向数据的因果推理的联合混合效应模型。
Stat Med. 2018 Feb 28;37(5):829-846. doi: 10.1002/sim.7567. Epub 2017 Dec 4.
3
Mediterranean diet impact on cardiovascular diseases: a narrative review.地中海饮食对心血管疾病的影响:叙事性综述。
J Cardiovasc Med (Hagerstown). 2017 Dec;18(12):925-935. doi: 10.2459/JCM.0000000000000573.
4
Comparison of Machine Learning Algorithms for the Prediction of Preventable Hospital Readmissions.用于预测可预防的医院再入院的机器学习算法比较
J Healthc Qual. 2018 May/Jun;40(3):129-138. doi: 10.1097/JHQ.0000000000000080.
5
Comparison of Propensity Score Methods and Covariate Adjustment: Evaluation in 4 Cardiovascular Studies.倾向评分法与协变量调整的比较:4 项心血管研究中的评估。
J Am Coll Cardiol. 2017 Jan 24;69(3):345-357. doi: 10.1016/j.jacc.2016.10.060.
6
Methodologic quality of meta-analyses and systematic reviews on the Mediterranean diet and cardiovascular disease outcomes: a review.荟萃分析和系统评价地中海饮食与心血管疾病结局的方法学质量:综述。
Am J Clin Nutr. 2016 Mar;103(3):841-50. doi: 10.3945/ajcn.115.112771. Epub 2016 Feb 10.
7
Novel Longitudinal and Propensity Score Matched Analysis of Hands-On Cooking and Nutrition Education versus Traditional Clinical Education among 627 Medical Students.627名医学生中实践烹饪与营养教育对比传统临床教育的新型纵向及倾向评分匹配分析
Adv Prev Med. 2015;2015:656780. doi: 10.1155/2015/656780. Epub 2015 Sep 8.
8
Causal Inference in the Age of Decision Medicine.决策医学时代的因果推断
J Data Mining Genomics Proteomics. 2015 Jan;6(1). doi: 10.4172/2153-0602.1000163.
9
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10
The effect of Mediterranean diet on the development of type 2 diabetes mellitus: a meta-analysis of 10 prospective studies and 136,846 participants.地中海饮食对2型糖尿病发生发展的影响:一项对10项前瞻性研究和136,846名参与者的荟萃分析。
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医学实习生饮食与营养咨询能力的地区差异:一项前瞻性多中心队列研究的机器学习增强倾向评分分析

Regional variations in medical trainee diet and nutrition counseling competencies: Machine learning-augmented propensity score analysis of a prospective multi-site cohort study.

作者信息

Patnaik Anish, Tran Justin, McWhorter John W, Burks Helen, Ngo Alexandra, Nguyen Tu Dan, Mody Avni, Moore Laura, Hoelscher Deanna M, Dyer Amber, Sarris Leah, Harlan Timothy, Chassay C Mark, Monlezun Dominique

机构信息

McGovern Medical School, University of Texas Health Sciences Center at Houston (UTHealth), Houston, TX USA.

School of Public Health, University of Texas Health Sciences Center at Houston (UTHealth), Houston, TX USA.

出版信息

Med Sci Educ. 2020 May 20;30(2):911-915. doi: 10.1007/s40670-020-00973-6. eCollection 2020 Jun.

DOI:10.1007/s40670-020-00973-6
PMID:34457749
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8368255/
Abstract

BACKGROUND

Medical professionals and students are inadequately trained to respond to rising global obesity and nutrition-related chronic disease epidemics, primarily focusing on cardiovascular disease. Yet, there are no multi-site studies testing evidence-based nutrition education for medical students in preventive cardiology, let alone establishing student dietary and competency patterns.

METHODS

Cooking for Health Optimization with Patients (CHOP; NIH NCT03443635) was the first multi-national cohort study using hands-on cooking and nutrition education as preventive cardiology, monitoring and improving student diets and competencies in patient nutrition education. Propensity-score adjusted multivariable regression was augmented by 43 supervised machine learning algorithms to assess students outcomes from UT Health versus the remaining study sites.

RESULTS

3,248 medical trainees from 20 medical centers and colleges met study criteria from 1 August 2012 to 31 December 2017 with 60 (1.49%) being from UTHealth. Compared to the other study sites, trainees from UTHealth were more likely to consume vegetables daily (OR 1.82, 95%CI 1.04-3.17, p=0.035), strongly agree that nutrition assessment should be routine clinical practice (OR 2.43, 95%CI 1.45-4.05, p=0.001), and that providers can improve patients' health with nutrition education (OR 1.73, 95%CI 1.03-2.91, p=0.038). UTHealth trainees were more likely to have mastered 12 of the 25 competency topics, with the top three being moderate alcohol intake (OR 1.74, 95%CI 0.97-3.11, p=0.062), dietary fats (OR 1.26, 95%CI 0.57-2.80, p=0.568), and calories (OR 1.26, 95%CI 0.70-2.28, p=0.446).

CONCLUSION

This machine learning-augmented causal inference analysis provides the first results that compare medical students nationally in their diets and competencies in nutrition education, highlighting the results from UTHealth. Additional studies are required to determine which factors in the hands-on cooking and nutrition curriculum for UTHealth and other sites produce optimal student - and, eventually, preventive cardiology - outcomes when they educate patients in those classes.

摘要

背景

医疗专业人员和学生在应对全球肥胖率上升和营养相关慢性病流行方面的培训不足,主要侧重于心血管疾病。然而,尚无多中心研究测试针对预防心脏病学专业医学生的循证营养教育,更不用说确立学生的饮食和能力模式了。

方法

“与患者一起进行健康烹饪优化”(CHOP;美国国立卫生研究院NCT03443635)是第一项多国队列研究,将实践烹饪和营养教育作为预防心脏病学的内容,监测并改善学生的饮食以及他们在患者营养教育方面的能力。倾向得分调整后的多变量回归通过43种监督机器学习算法进行增强,以评估德克萨斯大学健康科学中心(UT Health)的学生与其他研究地点的学生的结果。

结果

2012年8月1日至2017年12月31日,来自20个医疗中心和学院的3248名医学实习生符合研究标准,其中60名(1.49%)来自UT Health。与其他研究地点相比,UT Health的实习生更有可能每天食用蔬菜(比值比1.82,95%置信区间1.04 - 3.17,p = 0.035),强烈同意营养评估应成为常规临床实践(比值比2.43,95%置信区间1.