Background & Aims: Quality esophageal high-resolution manometry (HRM) studies require competent interpretation of data. However, there is little understanding of learning curves, training requirements, or measures of competency for HRM. We aimed to develop and use a competency assessment system to examine learning curves for interpretation of HRM data.
Methods: We conducted a prospective multicenter study of 20 gastroenterology trainees with no experience in HRM, from 8 centers, over an 8-month period (May through December 2015). We designed a web-based HRM training and competency assessment system. After reviewing the training module, participants interpreted 50 HRM studies and received answer keys at the fifth and then at every second interpretation. A cumulative sum procedure produced individual learning curves with preset acceptable failure rates of 10%; we classified competency status as competency not achieved, competency achieved, or competency likely achieved.
Results: Five (25%) participants achieved competence, 4 (20%) likely achieved competence, and 11 (55%) failed to achieve competence. A minimum case volume to achieve competency was not identified. There was no significant agreement between diagnostic accuracy and accuracy for individual HRM skills.
Conclusions: We developed a competency assessment system for HRM interpretation; using this system, we found significant variation in learning curves for HRM diagnosis and individual skills. Our system effectively distinguished trainee competency levels for HRM interpretation and contrary to current recommendations, found that competency for HRM is not case-volume specific.
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http://dx.doi.org/10.1016/j.cgh.2016.07.024 | DOI Listing |
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Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, P.R. China.
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January 2025
College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, China.
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January 2025
Harbin Medical University, Harbin, Heilongjiang Province, China.
Interstitial lung disease (ILD) is known to be a major complication of systemic sclerosis (SSc) and a leading cause of death in SSc patients. As the most common type of ILD, the pathogenesis of idiopathic pulmonary fibrosis (IPF) has not been fully elucidated. In this study, weighted correlation network analysis (WGCNA), protein‒protein interaction, Kaplan-Meier curve, univariate Cox analysis and machine learning methods were used on datasets from the Gene Expression Omnibus database.
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January 2025
Department of MRI, Zhongshan City People's Hospital, No. 2, Sunwen East Road, Shiqi District, Zhongshan, 528403, Guangdong, China.
To investigate the potential of an MRI-based radiomic model in distinguishing malignant prostate cancer (PCa) nodules from benign prostatic hyperplasia (BPH)-, as well as determining the incremental value of radiomic features to clinical variables, such as prostate-specific antigen (PSA) level and Prostate Imaging Reporting and Data System (PI-RADS) score. A restrospective analysis was performed on a total of 251 patients (training cohort, n = 119; internal validation cohort, n = 52; and external validation cohort, n = 80) with prostatic nodules who underwent biparametric MRI at two hospitals between January 2018 and December 2020. A total of 1130 radiomic features were extracted from each MRI sequence, including shape-based features, gray-level histogram-based features, texture features, and wavelet features.
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