Background: Arthroscopy is one of the most common procedures performed by orthopedic surgeons. Virtual reality (VR) simulation in general surgery residency training has been increasing over the past decade, but it has seen little use in the field of orthopedic surgery.
Objective: To determine osteopathic orthopedic surgery residents' perceived value of having access to a VR simulator before performing an arthroscopic procedure on a live patient.
Methods: A survey was developed and sent to all US osteopathic orthopedic surgery residency programs to be disseminated to all of their current residents. The survey consisted of 12 questions, which included Likert-type scale responses and yes or no responses.
Results: Fifty-eight residents out of approximately 507 responded. Forty-one of 57 respondents (72%) were in year 1 of residency when they performed their first arthroscopy, and 53 of 57 (93%) were not very comfortable when they performed their first arthroscopy. With respect to VR simulator exposure, approximately 31of 51 (61%) reported no exposure to a VR simulator, and 40 of 50 (80%) reported that their program did not provide a skills laboratory where they could practice arthroscopy. Of 50 respondents, 37 (74%) believed that a skills laboratory was important, 28 (56%) believed that a resident should perform 1 to 10 arthroscopies in a skills laboratory before performing one in the operating room, 34 (60%) believed that skills acquired in a skills laboratory would transfer to the operating room, and 33 (66%) agreed that every residency program should provide a skills laboratory. However, 29 (58%) believed that a skills laboratory would not improve patient safety.
Conclusion: Osteopathic orthopedic surgery residents indicated that they would benefit from the addition of an arthroscopic skills laboratory with a VR simulator. Furthermore, they believed that the skills learned in the skills laboratory would transfer to the operating room and would increase their comfort level with the procedure.
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http://dx.doi.org/10.7556/jaoa.2018.146 | DOI Listing |
Int J Comput Assist Radiol Surg
January 2025
Advanced Medical Devices Laboratory, Kyushu University, Nishi-ku, Fukuoka, 819-0382, Japan.
Purpose: This paper presents a deep learning approach to recognize and predict surgical activity in robot-assisted minimally invasive surgery (RAMIS). Our primary objective is to deploy the developed model for implementing a real-time surgical risk monitoring system within the realm of RAMIS.
Methods: We propose a modified Transformer model with the architecture comprising no positional encoding, 5 fully connected layers, 1 encoder, and 3 decoders.
Am J Hum Genet
January 2025
Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; Institute of Human Genetics, University of Regensburg, 93053 Regensburg, Germany; Institute of Clinical Human Genetics, University Hospital Regensburg, 93053 Regensburg, Germany. Electronic address:
BCL11B is a Cys2-His2 zinc-finger (C2H2-ZnF) domain-containing, DNA-binding, transcription factor with established roles in the development of various organs and tissues, primarily the immune and nervous systems. BCL11B germline variants have been associated with a variety of developmental syndromes. However, genotype-phenotype correlations along with pathophysiologic mechanisms of selected variants mostly remain elusive.
View Article and Find Full Text PDFObjective: To determine if surgical skills instructors' experience and qualifications influence students' learning of small animal ovariohysterectomy on a model (mOVH).
Sample Population: Second-year veterinary students (n = 105).
Methods: Students were randomized to three groups, taught by: (1) residency-trained surgeons with over 3 years' experience teaching mOVH, (2) general practitioners with over 3 years' experience teaching mOVH (GP >3), and (3) general practitioners with under 3 years' experience (GP <3).
Neural Netw
January 2025
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China; Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, 518060, China. Electronic address:
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.
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