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Williams DKA, Christophers B, Keyes T, Kumar R, Granovetter MC, Adigun A, Olivera J, Pura-Bryant J, Smith C, Okafor C, Shibre M, Daye D, Akabas MH. Sociodemographic factors and research experience impact MD-PhD program acceptance. JCI Insight 2024; 9:e176146. [PMID: 38329127 DOI: 10.1172/jci.insight.176146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024] Open
Abstract
The 2014 NIH Physician-Scientist Workforce Working Group predicted a future shortage of physician-scientists. Subsequent studies have highlighted disparities in MD-PhD admissions based on race, income, and education. Our analysis of data from the Association of American Medical Colleges covering 2014-2021 (15,156 applicants and 6,840 acceptees) revealed that acceptance into US MD-PhD programs correlates with research experience, family income, and research publications. The number of research experiences associated with parental education and family income. Applicants were more likely to be accepted with a family income greater than $50,000 or with one or more publications or presentations. Applicants were less likely to be accepted if they had parents without a graduate degree, were Black/African American, were first-generation college students, or were reapplicants, irrespective of the number of research experiences, publications, or presentations. These findings underscore an admissions bias that favors candidates from affluent and highly educated families, while disadvantaging underrepresented minorities.
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Affiliation(s)
- Darnell K Adrian Williams
- Albert Einstein College of Medicine, Medical Scientist Training Program, Bronx, New York, USA
- Albert Einstein College of Medicine, Bronx, New York, USA
- American Physician Scientists Association, Westford, Massachusetts, USA
| | - Briana Christophers
- American Physician Scientists Association, Westford, Massachusetts, USA
- Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, New York, New York, USA
| | - Timothy Keyes
- American Physician Scientists Association, Westford, Massachusetts, USA
- Stanford University Medical Scientist Training Program, Stanford, California, USA
| | - Rachit Kumar
- American Physician Scientists Association, Westford, Massachusetts, USA
- Perelman School of Medicine at the University of Pennsylvania, Medical Scientist Training Program, Philadelphia, Pennsylvania, USA
| | - Michael C Granovetter
- American Physician Scientists Association, Westford, Massachusetts, USA
- University of Pittsburgh-Carnegie Mellon University Medical Scientist Training Program, Pittsburgh, Pennsylvania, USA
| | - Alexandria Adigun
- American Physician Scientists Association, Westford, Massachusetts, USA
- Perelman School of Medicine at the University of Pennsylvania, Medical Scientist Training Program, Philadelphia, Pennsylvania, USA
| | - Justin Olivera
- Albert Einstein College of Medicine, Bronx, New York, USA
- American Physician Scientists Association, Westford, Massachusetts, USA
| | - Jehron Pura-Bryant
- Albert Einstein College of Medicine, Bronx, New York, USA
- American Physician Scientists Association, Westford, Massachusetts, USA
| | - Chynna Smith
- Albert Einstein College of Medicine, Medical Scientist Training Program, Bronx, New York, USA
- Albert Einstein College of Medicine, Bronx, New York, USA
- American Physician Scientists Association, Westford, Massachusetts, USA
| | - Chiemeka Okafor
- American Physician Scientists Association, Westford, Massachusetts, USA
- Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Mahlet Shibre
- Albert Einstein College of Medicine, Bronx, New York, USA
| | - Dania Daye
- American Physician Scientists Association, Westford, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Division of Interventional Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Myles H Akabas
- Albert Einstein College of Medicine, Medical Scientist Training Program, Bronx, New York, USA
- Albert Einstein College of Medicine, Bronx, New York, USA
- Departments of Neuroscience and Medicine, Albert Einstein College of Medicine, Bronx, New York, USA
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Takahashi K, Usuzaki T, Inamori R. Medical Statistics Unlock the Gateway to Further Research: Using Deep Learning to Predict CDKN2A/B Homozygous Deletion in Isocitrate Dehydrogenase-Mutant Astrocytoma. Korean J Radiol 2023; 24:1303-1305. [PMID: 38016690 DOI: 10.3348/kjr.2023.0925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 09/25/2023] [Indexed: 11/30/2023] Open
Affiliation(s)
- Kengo Takahashi
- Department of Diagnostic Imaging, Tohoku University Graduate School of Medicine, Miyagi, Japan.
| | - Takuma Usuzaki
- Department of Diagnostic Radiology, Tohoku University Hospital, Miyagi, Japan
| | - Ryusei Inamori
- Department of Diagnostic Imaging, Tohoku University Graduate School of Medicine, Miyagi, Japan
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AbdulRaheem Y. Statistics in medical research: Common mistakes. J Taibah Univ Med Sci 2023; 18:1197-1199. [PMID: 37234723 PMCID: PMC10205532 DOI: 10.1016/j.jtumed.2023.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 04/12/2023] [Indexed: 05/28/2023] Open
Abstract
The misuse of statistics in medical studies has been discussed extensively with the conclusion that it is both unethical and can have serious clinical consequences. These errors can contribute to incorrect conclusions, compromise the validity of studies, and overestimate or underestimate the effects of treatment. To avoid making these errors, it is critical to consider their presence and understand statistical concepts. This practice will ultimately lead to the use of appropriate statistical techniques for specific research questions and the calculation of an appropriate sample size to guarantee adequate statistical power. Common statistical errors in medical research include sampling bias, the incorrect determination of sample, failing to adjust for multiple comparisons, misinterpreting p-values as a measure of effect size or clinical relevance, choosing incorrect tests for a particular data set, type I and II errors, data fishing, and publication bias. It is important that researchers interpret their results using appropriate statistical concepts by soliciting feedback from specialist statisticians.
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4
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Hagemeier A, Samel C, Hellmich M. The regression discontinuity design: Methods and implementation with a worked example in health services research. Z Evid Fortbild Qual Gesundhwes 2022; 172:71-77. [PMID: 35718728 DOI: 10.1016/j.zefq.2022.04.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/29/2022] [Accepted: 04/12/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND The randomized controlled trial (RCT) is the gold standard in evidence-based medicine. However, this design may not be appropriate in every setting, so other methods or designs such as the regression discontinuity design (RDD) are required. METHOD The aim of this article is to introduce the RDD, summarise methodology in the context of health services research and present a worked example using the statistic software SPSS (Examples for R and Stata in the Appendix A). The mathematical notations of sharp and fuzzy RDD as well as their distinction are presented. Furthermore, examples from the literature and recent studies are highlighted, and both advantages and disadvantages of the design are discussed. APPLICATION The RDD consists of four essential steps: 1. Determine feasibility; 2. Note possible treatment manipulation, 3. Check for the treatment effect, and 4. Fit the regression models to measure the treatment effect. CONCLUSION The RDD comes as an alternative for studies in health service research where an RCT cannot be conducted, but a threshold-based comparison can be made.
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Affiliation(s)
- Anna Hagemeier
- Institute of Medical Statistics and Computational Biology, Medical Faculty, University of Cologne, University Hospital Cologne, Cologne, Germany.
| | - Christina Samel
- Institute of Medical Statistics and Computational Biology, Medical Faculty, University of Cologne, University Hospital Cologne, Cologne, Germany
| | - Martin Hellmich
- Institute of Medical Statistics and Computational Biology, Medical Faculty, University of Cologne, University Hospital Cologne, Cologne, Germany
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5
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Wang C, Li J, Li H, Xia Y, Wang X, Xie Y, Wu J. Learning from errors? The impact of erroneous example elaboration on learning outcomes of medical statistics in Chinese medical students. BMC Med Educ 2022; 22:469. [PMID: 35710473 PMCID: PMC9203230 DOI: 10.1186/s12909-022-03460-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 05/11/2022] [Indexed: 02/08/2023]
Abstract
Background Constructivism theory has suggested that constructing students’ own meaning is essential to successful learning. The erroneous example can easily trigger learners’ confusion and metacognition, which may “force” students to process the learning material and construct meaning deeply. However, some learners exhibit a low level of elaboration activity and spend little time on each example. Providing instructional scaffolding and elaboration training may be an efficient method for addressing this issue. The current study conducted a randomized controlled trial to examine the effectiveness of erroneous example elaboration training on learning outcomes and the mediating effects of metacognitive load for Chinese students in medical statistics during the COVID-19 pandemic. Methods Ninety-one third-year undergraduate medical students were randomly assigned to the training group (n = 47) and the control group (n = 44). Prerequisite course performance and learning motivation were collected as covariates. The mid-term exam and final exam were viewed as posttest and delayed-test to make sure the robustness of the training effect. The metacognitive load was measured as a mediating variable to explain the relationship between the training and academic performance. Results The training significantly improved both posttest and delayed-test performance compared with no training (Fposttest = 26.65, p < 0.001, Partial η2 = 0.23; Fdelayed test = 38.03, p < 0.001, Partial η2 = 0.30). The variation trend in metacognitive load in the two groups was significantly different (F = 2.24, p < 0.05, partial η2 = 0.20), but metacognitive load could not explain the positive association between the treatment and academic performance (β = − 0.06, se = 0.24, 95% CI − 0.57 to 0.43). Conclusions Erroneous example learning and metacognitive demonstrations are effective for academic performance in the domain of medical statistics, but their underlying mechanism merits further study.
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Affiliation(s)
- Chengwei Wang
- Department of Integrated Traditional and Western Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Junyi Li
- College of Psychology, Sichuan Normal University, Chengdu, China.
| | - Haiyan Li
- College of Psychology, Sichuan Normal University, Chengdu, China
| | - Yijing Xia
- College of Psychology, Sichuan Normal University, Chengdu, China
| | - Xiaoyu Wang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yufei Xie
- Department of Orthodontics, Shanghai Xuhui District Dental Disease Prevention and Control Institute, Shanghai, China
| | - Jinyang Wu
- Department of Oral and Cranio-maxillofacial Surgery, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China.,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China.,Shanghai Key Laboratory of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases, Shanghai, China
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Li S, Wang P, Li L. Response to concerns about the interpretation of subgroup analysis. J Clin Invest 2021; 132:156711. [PMID: 34855623 PMCID: PMC8759787 DOI: 10.1172/jci156711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Affiliation(s)
- Shilong Li
- Sema4, Stamford, United States of America
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, United States of America
| | - Li Li
- Clinical Informatics, Sema4, Stamford, United States of America
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Abstract
Dear Editor,We read with interest the article by Li and colleagues on the association between ACE inhibitors/angiotensin receptor blockers (ACE-I/ARB) use and in-hospital mortality among COVID-19 patients (1). The authors concluded that the use of ARB was associated with a significant reduction in in-hospital mortality among African American (AA) patients but not non-AA patients.However, we believe this conclusion is not per statistical principles and potentially misguiding readers. As noted by Altman and Bland (2), statistical analysis should be targeted to the clinical question: is the association between ARB use and in-hospital mortality different between AA and non-AA patients? To answer this question, one should directly compare the estimates (interaction test) (2), performed and reported by the authors. Here we argue that they did not accurately interpret this analysis.The authors showed an odds ratio (OR) of 0.196 (95% confidence interval [CI], 0.074 - 0.516) in the AA population and an OR of 0.687 (95% CI, 0.427 - 1.106) in the non-AA population. Accordingly, the interaction term was non-significant (95% CI, 0.185-1.292; P = 0.149).[1] As the authors stated that "Statistical significance was defined as a 2-sided P value less than 0.05", the correct interpretation of this result would be: the association of ACEi/ARB use and in-hospital mortality was not significantly different between these two populations (2). In contrast to this interpretation, the authors concluded that the association was only present in the AA population, which is not compatible with their analysis.The potential association between ACEi/ARB use and COVID-19 in-hospital mortality is of great interest to the medical community. Further, the ability to provide reliable subgroup analyses is vital in clinical decision-making (3). Interaction analyses are essential to answer the clinically relevant question of whether a specific subgroup of patients can benefit more from an intervention. However, we believe the correct interpretation of these results does not support the author's conclusion.
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Affiliation(s)
- Arthur M Albuquerque
- School of Medicine, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Carolina B Santolia
- School of Medicine, Universidade Estadual do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Ashish Verma
- Renal Division, Boston University School of Medicine, Boston, United States of America
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8
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Astrup BS, Kreiner S, Lauritsen J. Comment to the article: Discriminating between consensual intercourse and sexual assault: Genital-anal injury pattern in females. J Forensic Leg Med 2021; 81:102201. [PMID: 34144466 DOI: 10.1016/j.jflm.2021.102201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 05/31/2021] [Indexed: 10/21/2022]
Affiliation(s)
- Birgitte Schmidt Astrup
- Institute of Forensic Medicine, University of Southern Denmark, J.B. Winsløvs vej 17, Odense C, Denmark.
| | - Svend Kreiner
- Institute of Public Health, University of Copenhagen, Denmark
| | - Jens Lauritsen
- Accident Analysis Group, Ortopedic Dept. Odense University Hospital, J.B. Winsløws Vej 4, DK 5000, Odense C, Denmark; Dept. of Clinical Medicine, University of Southern Denmark, Odense, Denmark
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9
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Cohen SI. Oliver Wendell Holmes’ 1836 doctorate dissertation and his journey in medicine. World J Cardiol 2020; 12:362-367. [PMID: 32879700 PMCID: PMC7439451 DOI: 10.4330/wjc.v12.i8.362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 07/07/2020] [Accepted: 07/19/2020] [Indexed: 02/06/2023] Open
Abstract
Oliver Wendell Holmes’ 1836 hand written doctorate dissertation on acute pericarditis was discovered in the archives of the Boston Medical Library 101 years after it was successfully defended. It was then printed as an unabridged monograph with an explanation of its provenance. The dissertation has received little scrutiny since then. Holmes gathered materials for the scholarly work while he was a third and fourth year student at Ecole de Medecine in Paris. His mentor, Pierre-Charles-Alexandre- Louis insisted on the meticulous gathering and recording of every patient’s history and findings. Each category of data was given a weighted numerical value of diagnostic importance and the information was placed in a registry. Holmes became a disciple of Louis in gathering data by direct observation and measuring outcomes in a “statistical” fashion. Holmes dissertation on acute pericarditis describes the state of knowledge about the illness in the 1830s. When Holmes and other students who had studied in Paris returned to the United States, they helped turn American Medicine from opinion and strong personal bias toward scientific objectivity. Oliver Wendell Holmes eventually became both a professor of anatomy/physiology and a dean at Harvard Medical School. He is recognized as a leader in medicine and a popular author in America and beyond. In his late and infirmed years, Holmes questioned the wisdom of his unswerving advocacy for the scientific underpinnings of medicine. In retrospect he had overlooked the importance of also advocating that each patient be approached with comforting compassion.
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Affiliation(s)
- Stafford I Cohen
- Division of Cardiology, Beth Israel Deaconess Medical Center and Harvard Medical School, MA 02215, United States
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10
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Tian Q, Wang A, Zuo Y, Chen S, Hou H, Wang W, Wu S, Wang Y. All-cause mortality in metabolically healthy individuals was not predicted by overweight and obesity. JCI Insight 2020; 5:136982. [PMID: 32663197 PMCID: PMC7455121 DOI: 10.1172/jci.insight.136982] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 07/08/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Metabolically healthy obesity (MHO) and metabolically healthy overweight (MH-OW) have been suggested to be important and emerging phenotypes with an increased risk of cardiovascular disease (CVD). However, whether MHO and MH-OW are associated with all-cause mortality remains inconsistent. METHODS The association of MHO and MH-OW and all-cause mortality was determined in a Chinese community-based prospective cohort study (the Kailuan study), including 93,272 adults at baseline. Data were analyzed from 2006 to 2017. Participants were categorized into 6 mutually exclusive groups, according to BMI and metabolic syndrome (MetS) status. The primary outcome was all-cause death, and accidental deaths were excluded. RESULTS During a median follow-up of 11.04 years (interquartile range, 10.74–11.22 years), 8977 deaths occurred. Compared with healthy participants with normal BMI (MH-NW), MH-OW participants had the lowest risk of all-cause mortality (multivariate-adjusted HR [aHR], 0.926; 95% CI, 0.861–0.997), whereas there was no increased or decreased risk for MHO (aHR, 1.009; 95% CI, 0.886–1.148). Stratified analyses and sensitivity analyses further validated that there was a nonsignificant association between MHO and all-cause mortality. CONCLUSIONS Overweight and obesity do not predict increased risk of all-cause mortality in metabolic healthy Chinese individuals. FUNDING National Natural Science Foundation of China (NSFC; 81673247, 81872682 and 81773527), the NSFC Joint Project, and the Australian National Health and Medical Research Council (NHMRC; NSFC 81561128020-NHMRC APP1112767). Obesity does not predict increased risk of all-cause mortality in metabolic healthy Chinese individuals.
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Affiliation(s)
- Qiuyue Tian
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health
| | - Anxin Wang
- Department of Neurology, Beijing Tiantan Hospital, and.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yingting Zuo
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health
| | - Shuohua Chen
- Department of Cardiology, Kailuan General Hospital, North China University of Science and Technology, Tangshan, China
| | - Haifeng Hou
- School of Public Health, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an, China
| | - Wei Wang
- School of Public Health, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an, China.,School of Medical and Health Sciences, Edith Cowan University, Perth, Western Australia, Australia
| | - Shouling Wu
- Department of Cardiology, Kailuan General Hospital, North China University of Science and Technology, Tangshan, China
| | - Youxin Wang
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health
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Li S, Jiang L, Li X, Lin F, Wang Y, Li B, Jiang T, An W, Liu S, Liu H, Xu P, Zhao L, Zhang L, Mu J, Wang H, Kang J, Li Y, Huang L, Zhu C, Zhao S, Lu J, Ji J, Zhao J. Clinical and pathological investigation of patients with severe COVID-19. JCI Insight 2020; 5:138070. [PMID: 32427582 DOI: 10.1172/jci.insight.138070] [Citation(s) in RCA: 182] [Impact Index Per Article: 45.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 05/06/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), has become a pandemic. This study addresses the clinical and immunopathological characteristics of severe COVID-19. METHODS Sixty-nine patients with COVID-19 were classified into severe and nonsevere groups to analyze their clinical and laboratory characteristics. A panel of blood cytokines was quantified over time. Biopsy specimens from 2 deceased cases were obtained for immunopathological, ultrastructural, and in situ hybridization examinations. RESULTS Circulating cytokines, including IL-8, IL-6, TNF-α, IP10, MCP1, and RANTES, were significantly elevated in patients with severe COVID-19. Dynamic IL-6 and IL-8 were associated with disease progression. SARS-CoV-2 was demonstrated to infect type II and type I pneumocytes and endothelial cells, leading to severe lung damage through cell pyroptosis and apoptosis. In severe cases, lymphopenia, neutrophilia, depletion of CD4+ and CD8+ T lymphocytes, and massive macrophage and neutrophil infiltrates were observed in both blood and lung tissues. CONCLUSIONS A panel of circulating cytokines could be used to predict disease deterioration and inform clinical interventions. Severe pulmonary damage was predominantly attributed to both cytopathy caused by SARS-CoV-2 and immunopathologic damage. Strategies that prohibit pulmonary recruitment and overactivation of inflammatory cells by suppressing cytokine storm might improve the outcomes of patients with severe COVID-19.
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Affiliation(s)
| | | | - Xi Li
- Department of Pathology and Hepatology
| | - Fang Lin
- Department of Intensive Care Unit
| | | | - Boan Li
- Department of Clinical Laboratory
| | | | - Weimin An
- Department of Radiology, The Fifth Medical Center of PLA General Hospital, Beijing, China
| | | | | | | | | | | | | | - Hongwei Wang
- Department of Pathology, The Fourth Medical Center of PLA General Hospital, Beijing, China
| | - Jiarui Kang
- Department of Pathology, The Fourth Medical Center of PLA General Hospital, Beijing, China
| | - Yan Li
- Department of Pathology and Hepatology
| | - Lei Huang
- Department of Infectious Diseases, and
| | | | - Shousong Zhao
- Department of Infectious Diseases, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Jiangyang Lu
- Department of Pathology, The Fourth Medical Center of PLA General Hospital, Beijing, China
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12
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Zhou Y, Leung SW, Mizutani S, Takagi T, Tian YS. MEPHAS: an interactive graphical user interface for medical and pharmaceutical statistical analysis with R and Shiny. BMC Bioinformatics 2020; 21:183. [PMID: 32393166 PMCID: PMC7216538 DOI: 10.1186/s12859-020-3494-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 04/15/2020] [Indexed: 11/20/2022] Open
Abstract
Background Even though R is one of the most commonly used statistical computing environments, it lacks a graphical user interface (GUI) that appeals to students, researchers, lecturers, and practitioners in medicine and pharmacy for conducting standard data analytics. Current GUIs built on top of R, such as EZR and R-Commander, aim to facilitate R coding and visualization, but most of the functionalities are still accessed through a command-line interface (CLI). To assist practitioners of medicine and pharmacy and researchers to run most routines in fundamental statistical analysis, we developed an interactive GUI; i.e., MEPHAS, to support various web-based systems that are accessible from laptops, workstations, or tablets, under Windows, macOS (and IOS), or Linux. In addition to fundamental statistical analysis, advanced statistics such as the extended Cox regression and dimensional analyses including partial least squares regression (PLS-R) and sparse partial least squares regression (SPLS-R), are also available in MEPHAS. Results MEPHAS is a web-based GUI (https://alain003.phs.osaka-u.ac.jp/mephas/) that is based on a shiny framework. We also created the corresponding R package mephas (https://mephas.github.io/). Thus far, MEPHAS has supported four categories of statistics, including probability, hypothesis testing, regression models, and dimensional analyses. Instructions and help menus were accessible during the entire analytical process via the web-based GUI, particularly advanced dimensional data analysis that required much explanation. The GUI was designed to be intuitive for non-technical users to perform various statistical functions, e.g., managing data, customizing plots, setting parameters, and monitoring real-time results, without any R coding from users. All generated graphs can be saved to local machines, and tables can be downloaded as CSV files. Conclusion MEPHAS is a free and open-source web-interactive GUI that was designed to support statistical data analyses and prediction for medical and pharmaceutical practitioners and researchers. It enables various medical and pharmaceutical statistical analyses through interactive parameter settings and dynamic visualization of the results.
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Affiliation(s)
- Yi Zhou
- Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita City, Osaka, 565-0871, Japan
| | - Siu-Wai Leung
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China.,School of Informatics, College of Science and Engineering, University of Edinburgh, Edinburgh, UK
| | - Shosuke Mizutani
- Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita City, Osaka, 565-0871, Japan
| | - Tatsuya Takagi
- Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita City, Osaka, 565-0871, Japan
| | - Yu-Shi Tian
- Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita City, Osaka, 565-0871, Japan.
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Abstract
At the center of this work stands the anthropometric research program during World War I for studying constitutional medicine and the connected series of investigations by the medical internists Theodor Brugsch, Hermann Rautmann and Max Berliner, their advances in the statistics of variability as well as the subsequent debate in constitutional medicine and pathology on the definition of the physical norm.In order to create a data basis for the "normal" body in the study of constitutional medicine, a series of young German internists undertook comprehensive anthropometric studies in the context of World War I, thereby taking advantage of the opportunity offered them by war to conduct a series of examinations of soldiers, but without having first reflected on methods of measurement, comparison, and evaluation. At the same time, the concept of the "normal" body, then only vaguely formed, still needed to be critically expounded. However, this changed during the subsequent period and led not only to a stronger emphasis on methodology, rather also to greater competency in mathematical statistics and philosophical cogitation on the meaning of the "norm". In this way, constitutional medicine originated the potent medical norm debate of the early 1920s which still resonates in medical theory today. By this means the few years following the end of World War I not only represented a turning point for constitutional medicine regarding the reflective use of methodology, but also introduced a new orientation of their research questions: away from the "normal" body to individuality.
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Affiliation(s)
- Nadine Metzger
- Lehrstuhl für Geschichte der Medizin, Friedrich-Alexander-Universität Erlangen-Nürnberg, Glückstr. 10, 91054, Erlangen, Deutschland.
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14
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Abstract
Length of time is a variable often encountered during data analysis. Survival analysis provides simple, intuitive results concerning time-to-event for events of interest, which are not confined to death. This review introduces methods of analyzing time-to-event. The Kaplan-Meier survival analysis, log-rank test, and Cox proportional hazards regression modeling method are described with examples of hypothetical data.
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Affiliation(s)
- Junyong In
- Department of Anesthesiology and Pain Medicine, Dongguk University Ilsan Hospital, Goyang, Korea
| | - Dong Kyu Lee
- Guro Hospital, Korea University School of Medicine, Seoul, Korea,Corresponding author: Dong Kyu Lee, M.D., Ph.D. Department of Anesthesiology and Pain Medicine, Guro Hospital, Korea University School of Medicine, 148, Gurodong-ro, Guro-gu, Seoul 08308, Korea Tel: 82-2-2626-3237, Fax: 82-2-2626-1438
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Luo L, Cheng X, Wang S, Zhang J, Zhu W, Yang J, Liu P. Blended learning with Moodle in medical statistics: an assessment of knowledge, attitudes and practices relating to e-learning. BMC Med Educ 2017; 17:170. [PMID: 28927383 PMCID: PMC5606039 DOI: 10.1186/s12909-017-1009-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 09/11/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND Blended learning that combines a modular object-oriented dynamic learning environment (Moodle) with face-to-face teaching was applied to a medical statistics course to improve learning outcomes and evaluate the impact factors of students' knowledge, attitudes and practices (KAP) relating to e-learning. METHODS The same real-name questionnaire was administered before and after the intervention. The summed scores of every part (knowledge, attitude and practice) were calculated using the entropy method. A mixed linear model was fitted using the SAS PROC MIXED procedure to analyse the impact factors of KAP. RESULTS Educational reform, self-perceived character, registered permanent residence and hours spent online per day were significant impact factors of e-learning knowledge. Introversion and middle type respondents' average scores were higher than those of extroversion type respondents. Regarding e-learning attitudes, educational reform, community number, Internet age and hours spent online per day had a significant impact. Specifically, participants whose Internet age was no greater than 6 years scored 7.00 points lower than those whose Internet age was greater than 10 years. Regarding e-learning behaviour, educational reform and parents' literacy had a significant impact, as the average score increased 10.05 points (P < 0.0001). CONCLUSIONS This educational reform that combined Moodle with a traditional class achieved good results in terms of students' e-learning KAP. Additionally, this type of blended course can be implemented in many other curriculums.
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Affiliation(s)
- Li Luo
- School of public health, Southeast University, Nanjing, 87 Dingjiaqiao China
| | - Xiaohua Cheng
- School of public health, Southeast University, Nanjing, 87 Dingjiaqiao China
| | - Shiyuan Wang
- School of public health, Southeast University, Nanjing, 87 Dingjiaqiao China
| | - Junxue Zhang
- School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Wenbo Zhu
- School of public health, Southeast University, Nanjing, 87 Dingjiaqiao China
| | - Jiaying Yang
- School of public health, Southeast University, Nanjing, 87 Dingjiaqiao China
| | - Pei Liu
- School of public health, Southeast University, Nanjing, 87 Dingjiaqiao China
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Westbury LD, Syddall HE, Simmonds SJ, Cooper C, Sayer AA. Identification of risk factors for hospital admission using multiple-failure survival models: a toolkit for researchers. BMC Med Res Methodol 2016; 16:46. [PMID: 27117081 PMCID: PMC4845493 DOI: 10.1186/s12874-016-0147-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Accepted: 03/18/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The UK population is ageing; improved understanding of risk factors for hospital admission is required. Linkage of the Hertfordshire Cohort Study (HCS) with Hospital Episode Statistics (HES) data has created a multiple-failure survival dataset detailing the characteristics of 2,997 individuals at baseline (1998-2004, average age 66 years) and their hospital admissions (regarded as 'failure events') over a 10 year follow-up. Analysis of risk factors using logistic regression or time to first event Cox modelling wastes information as an individual's admissions after their first are disregarded. Sophisticated analysis techniques are established to examine risk factors for admission in such datasets but are not commonly implemented. METHODS We review analysis techniques for multiple-failure survival datasets (logistic regression; time to first event Cox modelling; and the Andersen and Gill [AG] and Prentice, Williams and Peterson Total Time [PWP-TT] multiple-failure models), outline their implementation in Stata, and compare their results in an analysis of housing tenure (a marker of socioeconomic position) as a risk factor for different types of hospital admission (any; emergency; elective; >7 days). The AG and PWP-TT models include full admissions histories in the analysis of risk factors for admission and account for within-subject correlation of failure times. The PWP-TT model is also stratified on the number of previous failure events, allowing an individual's baseline risk of admission to increase with their number of previous admissions. RESULTS All models yielded broadly similar results: not owner-occupying one's home was associated with increased risk of hospital admission. Estimated effect sizes were smaller from the PWP-TT model in comparison with other models owing to it having accounted for an increase in risk of admission with number of previous admissions. For example, hazard ratios [HR] from time to first event Cox models were 1.67(95 % CI: 1.36,2.04) and 1.63(95 % CI:1.36,1.95) for not owner-occupying one's home in relation to risk of emergency admission or death among women and men respectively; corresponding HRs from the PWP-TT model were 1.34(95 % CI:1.15,1.56) for women and 1.23(95 % CI:1.07,1.41) for men. CONCLUSION The PWP-TT model may be implemented using routine statistical software and is recommended for the analysis of multiple-failure survival datasets which detail repeated hospital admissions among older people.
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Affiliation(s)
- Leo D Westbury
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK.
| | - Holly E Syddall
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Shirley J Simmonds
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Cyrus Cooper
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK.,NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK.,NIHR Musculoskeletal Biomedical Research Unit, University of Oxford, Oxford, UK
| | - Avan Aihie Sayer
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK.,NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK.,Academic Geriatric Medicine, Faculty of Medicine, University of Southampton, Southampton, UK.,NIHR Collaboration for Leadership in Applied Health Research and Care, Wessex, Southampton, UK.,Institute for Ageing and Institute of Health & Society, Newcastle University, Newcastle, UK
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Bertolaccini L, Viti A, Terzi A. The Statistical point of view of Quality: the Lean Six Sigma methodology. J Thorac Dis 2015; 7:E66-8. [PMID: 25973253 DOI: 10.3978/j.issn.2072-1439.2015.04.11] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2015] [Accepted: 02/28/2015] [Indexed: 11/14/2022]
Abstract
Six Sigma and Lean are two quality improvement methodologies. The Lean Six Sigma methodology is applicable to repetitive procedures. Therefore, the use of this methodology in the health-care arena has focused mainly on areas of business operations, throughput, and case management and has focused on efficiency outcomes. After the revision of methodology, the paper presents a brief clinical example of the use of Lean Six Sigma as a quality improvement method in the reduction of the complications during and after lobectomies. Using Lean Six Sigma methodology, the multidisciplinary teams could identify multiple modifiable points across the surgical process. These process improvements could be applied to different surgical specialties and could result in a measurement, from statistical point of view, of the surgical quality.
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Affiliation(s)
- Luca Bertolaccini
- 1 Thoracic Surgery Unit, Sacro Cuore-Don Calabria Research Hospital, Negrar Verona, Italy ; 2 Thoracic Surgery Unit, S. Croce e Carle Hospital, Cuneo, Italy
| | - Andrea Viti
- 1 Thoracic Surgery Unit, Sacro Cuore-Don Calabria Research Hospital, Negrar Verona, Italy ; 2 Thoracic Surgery Unit, S. Croce e Carle Hospital, Cuneo, Italy
| | - Alberto Terzi
- 1 Thoracic Surgery Unit, Sacro Cuore-Don Calabria Research Hospital, Negrar Verona, Italy ; 2 Thoracic Surgery Unit, S. Croce e Carle Hospital, Cuneo, Italy
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