In 2004, The George Washington University received funding from the U.S. Department of Homeland Security to develop a web-based emergency preparedness course for nurses. The purpose of the course was to provide training that would be accessible regardless of work setting or location. In designing the course, the development team used algorithmic decision making as a conceptual framework to transcend the linear, didactic focus of traditional online preparedness training to provide learners with a learning experience crafted around the decision-making process. This article describes the design of the algorithmic practice maps underlying this course and provides a replicable structure for those interested in developing similar offerings for nurses.
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http://dx.doi.org/10.3928/00220124-20111228-89 | DOI Listing |
Anal Chem
January 2025
Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian 350117, China.
Multiple myeloma is a hematologic malignancy characterized by the proliferation of abnormal plasma cells in the bone marrow. Despite therapeutic advancements, there remains a critical need for reliable, noninvasive methods to monitor multiple myeloma. Circulating plasma cells (CPCs) in peripheral blood are robust and independent prognostic markers, but their detection is challenging due to their low abundance.
View Article and Find Full Text PDFJ Neurosurg Spine
January 2025
2Cleveland Clinic Center for Spine Health, Cleveland Clinic, Cleveland; and.
Objective: Spinal fusion is a commonly performed surgical procedure used to relieve pain, deformity, and instability of various spinal pathologies. Although there have been attempts to standardize spinal fusion assessment radiologically, there is currently no unified definition that also considers clinical symptomology. This review attempts to create a more holistic and standardized definition of spinal fusion.
View Article and Find Full Text PDFUltrasound Obstet Gynecol
January 2025
Robinson Research Institute, University of Adelaide, Adelaide, Australia.
Objectives: The development of valuable artificial intelligence (AI) tools to assist with ultrasound diagnosis depends on algorithms developed using high-quality data. This study aimed to test the intra- and interobserver agreement of a proposed image-quality scoring system to quantify the quality of gynecological transvaginal ultrasound (TVS) images, which could be used in clinical practice and AI tool development.
Methods: A proposed scoring system to quantify TVS image quality was created following a review of the literature.
PLoS One
January 2025
Department of Radiology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
To evaluate the diagnostic accuracy of artificial intelligence (AI) assisted radiologists and standard double-reading in real-world clinical settings for rib fractures (RFs) detection on CT images. This study included 243 consecutive chest trauma patients (mean age, 58.1 years; female, 166) with rib CT scans.
View Article and Find Full Text PDFTransl Vis Sci Technol
January 2025
Glaucoma Service, Wills Eye Hospital, Philadelphia, PA, USA.
Purpose: The integration of artificial intelligence (AI), particularly deep learning (DL), with optical coherence tomography (OCT) offers significant opportunities in the diagnosis and management of glaucoma. This article explores the application of various DL models in enhancing OCT capabilities and addresses the challenges associated with their clinical implementation.
Methods: A review of articles utilizing DL models was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, and large language models (LLMs).
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