The US orthopaedic graduate medical education system is based on long established methods in education, but academic leaders at orthopaedic institutions now have the ability to use electronic learning innovations. Hospital for Special Surgery gathered graduate medical education leaders from orthopaedic training programs around the country and an electronic learning expert to review current orthopaedic residency and fellowship program practices. This group came to consensus with the following points: (1) current training methods do not take full advantage of available technology/innovations, (2) trainees inappropriately use electronic resources in the absence of or in an underdeveloped formal electronic training program, (3) trainees learn at different rates and in different ways requiring individualized plans for optimal content engagement, and (4) formal electronic learning programs better use time dedicated to educating trainees than informal programs. Orthopaedic graduate medical training programs that adopt these points can establish an electronic learning program to complement their traditional education program by (1) guaranteeing online content is standardized and approved, (2) reducing time spent covering standard lecture material and increasing time spent reviewing cases, and (3) engaging students of all learning backgrounds with content in both asynchronous and synchronous formats.
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http://dx.doi.org/10.5435/JAAOS-D-20-00821 | DOI Listing |
Vis Intell
December 2024
Department of Information Technology and Electrical Engineering, ETH Zurich, Sternwartstrasse 7, Zürich, Switzerland.
The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the popular LLM backbone of multi-modal large language models (MLLMs), widely used in computer vision and natural language understanding tasks. In particular, LLaMA3 models have recently been released and have achieved impressive performance in various domains with super-large scale pre-training on over 15T tokens of data. Given the wide application of low-bit quantization for LLMs in resource-constrained scenarios, we explore LLaMA3's capabilities when quantized to low bit-width.
View Article and Find Full Text PDFRisk Manag Healthc Policy
January 2025
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, New Taipei City, 235603, Taiwan.
Purpose: As HF progresses into advanced HF, patients experience a poor quality of life, distressing symptoms, intensive care use, social distress, and eventual hospital death. We aimed to investigate the relationship between morality and potential prognostic factors among in-patient and emergency patients with HF.
Patients And Methods: A case series study: Data are collected from in-hospital and emergency care patients from 2014 to 2021, including their international classification of disease at admission, and laboratory data such as blood count, liver and renal functions, lipid profile, and other biochemistry from the hospital's electrical medical records.
Math Biosci Eng
December 2024
Department of Electronics and Communication Engineering, Akshaya College of Engineering and Technology, Coimbatore, Tamil Nadu, India.
The hippocampus is a small, yet intricate seahorse-shaped tiny structure located deep within the brain's medial temporal lobe. It is a crucial component of the limbic system, which is responsible for regulating emotions, memory, and spatial navigation. This research focuses on automatic hippocampus segmentation from Magnetic Resonance (MR) images of a human head with high accuracy and fewer false positive and false negative rates.
View Article and Find Full Text PDFCurr Med Imaging
January 2025
School of Life Sciences, Tiangong University, Tianjin 300387, China.
Objective: The objective of this research is to enhance pneumonia detection in chest X-rays by leveraging a novel hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with modified Swin Transformer blocks. This study aims to significantly improve diagnostic accuracy, reduce misclassifications, and provide a robust, deployable solution for underdeveloped regions where access to conventional diagnostics and treatment is limited.
Methods: The study developed a hybrid model architecture integrating CNNs with modified Swin Transformer blocks to work seamlessly within the same model.
BMJ Open
December 2024
Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.
Introduction: Early lung cancer screening (LCS) through low-dose CT (LDCT) is crucial but underused due to various barriers, including incomplete or inaccurate patient smoking data in the electronic health record and limited time for shared decision-making. The objective of this trial is to investigate a patient-centred intervention, MyLungHealth, delivered through the patient portal. The intervention is designed to improve LCS rates through increased identification of eligible patients and informed decision-making.
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