Background: Surgical scrubbing, gowning, and gloving is challenging for medical trainees to learn in the operating room environment. Currently, there are few reliable or valid tools to evaluate a trainee's ability to scrub, gown and glove. The objective of this study is to test the reliability and validity of a checklist that evaluates the technique of surgical scrubbing, gowning and gloving (SGG).
Methods: This Institutional Review Board-approved study recruited medical students, residents, and fellows from an academic, tertiary care institution. Trainees were stratified based upon prior surgical experience as novices, intermediates, or experts. Participants were instructed to scrub, gown and glove in a staged operating room while being video-recorded. Two blinded raters scored the videos according to the SGG checklist. Reliability was assessed using the intraclass correlation coefficient for total scores and Cohen's kappa for item completion. The internal consistency and discriminant validity of the SGG checklist were assessed using Cronbach alpha and the Wilcoxon rank sum test, respectively.
Results: 56 participants were recruited (18 novices, 19 intermediates, 19 experts). The intraclass correlation coefficient demonstrated excellent inter-rater reliability for the overall checklist (0.990), and the Cohen's kappa ranged from 0.598 to 1.00. The checklist also had excellent internal consistency (Cronbach's alpha 0.950). A significant difference in scores was observed between all groups (p < 0.001).
Conclusion: This checklist demonstrates a high inter-rater reliability, discriminant validity, and internal consistency. It has the potential to enhance medical education curricula.
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http://dx.doi.org/10.5195/ijms.2021.1221 | DOI Listing |
Liver Int
February 2025
Liver Research Center, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Background And Aim: Discriminating between idiosyncratic drug-induced liver injury (DILI) and autoimmune hepatitis (AIH) is critical yet challenging. We aim to develop and validate a machine learning (ML)-based model to aid in this differentiation.
Methods: This multicenter cohort study utilised a development set from Beijing Friendship Hospital, with retrospective and prospective validation sets from 10 tertiary hospitals across various regions of China spanning January 2009 to May 2023.
Br J Soc Psychol
January 2025
School of Psychology, The University of Adelaide, Adelaide, Australia.
This article reports the development and validation of the Episodic Empowerment Scale (EES): A manipulation check designed to measure a momentary psychological state. In Study 1, participants (n = 125) completed a selection of candidate items after being exposed to a low- or high-power manipulation. Exploratory factor analysis was used to reduce the number of items to a brief five-item measure.
View Article and Find Full Text PDFJ Vasc Access
January 2025
College of Nursing, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Objective: To develop and validate a nomogram model for predicting central venous catheter-related infections (CRI) in patients with maintenance hemodialysis (MHD).
Methods: MHD patients with central venous catheters (CVCs) visiting the outpatient hemodialysis (HD) center of Xuzhou Medical University Affiliated Hospital from January 2020 to December 2023 were retrospectively selected through a HD monitoring system. Patient data were collected, and the patients were divided into training and validation sets in a 7:3 ratio.
Curr Med Chem
January 2025
Department of Electronics & Communication Engineering, Jaypee University of Information Technology, Solan, H.P., India.
A planktonic population of bacteria can form a biofilm by adhesion and colonization. Proteins known as "adhesins" can bind to certain environmental structures, such as sugars, which will cause the bacteria to attach to the substrate. Quorum sensing is used to establish the population is dense enough to form a biofilm.
View Article and Find Full Text PDFWorld J Gastrointest Oncol
January 2025
Department of Hepatobiliary and Pancreaticosplenic Surgery, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou 434100, Hubei Province, China.
Background: The liver, as the main target organ for hematogenous metastasis of colorectal cancer, early and accurate prediction of liver metastasis is crucial for the diagnosis and treatment of patients. Herein, this study aims to investigate the application value of a combined machine learning (ML) based model based on the multiparameter magnetic resonance imaging for prediction of rectal metachronous liver metastasis (MLM).
Aim: To investigate the efficacy of radiomics based on multiparametric magnetic resonance imaging images of preoperative first diagnosed rectal cancer in predicting MLM from rectal cancer.
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