Background: Endoscopic retrograde cholangiopancreatography (ERCP) in patients with surgically altered anatomy is technically difficult. Extensive training is required to develop the ability to perform this procedure.
Aims: To investigate the learning curve of single-balloon-assisted enteroscopy ERCP (SBE-ERCP).
Methods: We conducted a retrospective, observational case series at a single center. We evaluated the SBE-ERCP procedures between April 2011 and February 2021. The main outcomes were the rate of reaching the target site and the success rate of the entire procedure. These parameters were additionally expressed as a learning curve.
Results: A total of 687 SBE-ERCP procedures were analyzed. The learning curve was analyzed in blocks of 10 cases. In this study, seven endoscopists, experts in conventional ERCP, were included. The overall SBE-ERCP procedural success rate was 92.2% (634/687 cases). Combining all data from individual endoscopists' evaluation periods, the insertion and success rates of the SBE-ERCP procedures gradually increased with increased experience performing SBE-ERCP. The insertion success rates for the number of SBE-ERCP cases (< 20, 21-30, > 30) were 82.9%, 92.9%, and 94.3%, respectively; the procedure success rates were 74.3%, 81.4%, and 92.9%, respectively. The endoscopists who had performed > 30 SBE-ERCP cases had a success rate of ≥ 90%.
Conclusions: Our results suggest that performing > 30 cases is one of the targets for conventional ERCP experts to become competent in performing SBE-ERCP in patients with a surgically altered anatomy.
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http://dx.doi.org/10.1007/s10620-021-07342-2 | DOI Listing |
JMIR Med Inform
January 2025
Department of Science and Education, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, China.
Background: Large language models (LLMs) have been proposed as valuable tools in medical education and practice. The Chinese National Nursing Licensing Examination (CNNLE) presents unique challenges for LLMs due to its requirement for both deep domain-specific nursing knowledge and the ability to make complex clinical decisions, which differentiates it from more general medical examinations. However, their potential application in the CNNLE remains unexplored.
View Article and Find Full Text PDFJ Occup Health
January 2025
Panasonic Corporation, Department Electric Works Company/Engineering Division, Osaka, Japan.
Background: Falls are among the most prevalent workplace accidents, necessitating thorough screening for susceptibility to falls and customization of individualized fall prevention programs. The aim of this study was to develop and validate a high fall risk prediction model using machine learning (ML) and video-based first three steps in middle-aged workers.
Methods: Train data (n=190, age 54.
Esophagus
January 2025
Department of Surgery, Tohoku University Graduate School of Medicine, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8574, Japan.
Background: Neoadjuvant chemotherapy is standard for advanced esophageal squamous cell carcinoma, though often ineffective. Therefore, predicting the response to chemotherapy before treatment is desirable. However, there is currently no established method for predicting response to neoadjuvant chemotherapy.
View Article and Find Full Text PDFInsights Imaging
January 2025
Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
Introduction: A large number of middle-aged and elderly patients have an insufficient understanding of osteoporosis and its harm. This study aimed to establish and validate a convolutional neural network (CNN) model based on unenhanced chest computed tomography (CT) images of the vertebral body and skeletal muscle for opportunistic screening in patients with osteoporosis.
Materials And Methods: Our team retrospectively collected clinical information from participants who underwent unenhanced chest CT and dual-energy X-ray absorptiometry (DXA) examinations between January 1, 2022, and December 31, 2022, at four hospitals.
J Robot Surg
January 2025
Department of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510230, Guangdong, China.
This study applied cumulative sum (CUSUM) analysis to evaluate trends in operative time and blood loss, It aims to identify key milestones in mastering extraperitoneal single-site robotic-assisted radical prostatectomy (ss-RARP). A cohort of 100 patients who underwent ss-RARP, performed by a single surgeon at the First Affiliated Hospital of Guangzhou Medical University between March 2021 and June 2023, was retrospectively analyzed. To evaluate the learning curve, the CUSUM (Cumulative Sum Control Chart) technique was applied, revealing the progression and variability over time.
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