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A Modern Education Theory Approach to Development of a Benchtop Simulation Model for Ultrasound-Guided Percutaneous Nephrolithotomy.
J Endourol
December 2024
Department of Urology, University of California San Francisco, San Francisco, California, USA.
Article Synopsis
- Developed a nonbiohazardous simulator for ultrasound-guided percutaneous nephrolithotomy using expert feedback and modern education theory.
- A prototype was created with realistic anatomical features through 3D printing and hydrogel molding, and validated by expert surgeons and participants in multicenter trials.
- The simulator showed high satisfaction ratings and significant skill improvements for residents, making it an effective training tool for urologic education.
Background: Recent advancements of large language models (LLMs) like Generative Pre-trained Transformer 4 (GPT-4) have generated significant interest among the scientific community. Yet, the potential of these models to be utilized in clinical settings remains largely unexplored. This study investigated the abilities of multiple LLMs and traditional machine learning models to analyze emergency department (ED) reports and determine if the corresponding visits were caused by symptomatic kidney stones.
View Article and Find Full Text PDFThiazide Dose, Urine Calcium, and Symptomatic Kidney Stone Events.
JAMA Netw Open
August 2024
Department of Urology, NorthShore University Health System, Evanston, Illinois.
Evaluation of Genetic Associations with Clinical Phenotypes of Kidney Stone Disease.
Eur Urol Open Sci
September 2024
Department of Urology, Vanderbilt University Medical Center, Nashville, TN, USA.
Article Synopsis
- This study investigates the genetic factors contributing to kidney stone disease using a large-scale analysis of electronic health records from over 5,000 cases and 83,000 controls.
- The research identified ten significant genetic loci related to kidney stones, with the most notable one being rs28544423, which influences urinary excretion and is linked to calcium oxalate stones.
- While important genetic associations were found, the study noted some limitations including potential biases and concluded that genetic variants influence stone composition but not the severity of the disease.
PheMIME: an interactive web app and knowledge base for phenome-wide, multi-institutional multimorbidity analysis.
J Am Med Inform Assoc
November 2024
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
Article Synopsis
- PheMIME is an interactive visualization tool developed to analyze and characterize multimorbidity patterns across different populations using data from large-scale electronic health record (EHR) systems.
- It integrates data from institutions like Vanderbilt University and Mass General Brigham, allowing users to explore complex disease relationships through dynamic, multi-faceted visualizations and analyses.
- The tool enhances our understanding of patient health by making it easier to identify disease associations, ultimately contributing to more personalized healthcare strategies.
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