Showing 321 - 340 results of 593 for search '"veteran"', query time: 0.04s Refine Results
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    Th17 Cell Response in SOD1G93A Mice following Motor Nerve Injury by Allen Ni, Tao Yang, Nichole A. Mesnard-Hoaglin, Rafael Gutierrez, Evan B. Stubbs, Susan O. McGuire, Virginia M. Sanders, Kathryn J. Jones, Eileen M. Foecking, Junping Xin

    Published 2016-01-01
    “…An increased risk of ALS has been reported for veterans, varsity athletes, and professional football players. …”
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    A Retrospective Review of Thiazolidinediones with Development of a Troglitazone Conversion Protocol by Cameron Lindsey, Maqual Graham, Julie McMurphy

    Published 2003-01-01
    “…The objective of this paper was (1) to assess compliance with the National Veterans Affairs Guidelines for the use of troglitazone and rosiglitazone and (2) to develop and implement a conversion protocol that allows effective management of patients receiving troglitazone. …”
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    VA physicians intent to leave and correlations to drivers of burnout: a cross-sectional study by Robert C. Oh, David C. Mohr, Tamara M. Schult

    Published 2025-01-01
    “…The sample included 16,363 respondents from the Veterans Health Administration (VA). A multinomial model was run to compare physicians indicating turnover intent because they were: a.) changing internal jobs; b.) leaving the organization for another job; or c.) retiring, against physicians indicating they had no plans to turnover. …”
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    A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance by Wei-Wen Chen, Ling Kuo, Yi-Xun Lin, Wen-Chung Yu, Chien-Chao Tseng, Yenn-Jiang Lin, Ching-Chun Huang, Shih-Lin Chang, Jacky Chung-Hao Wu, Chun-Ku Chen, Ching-Yao Weng, Siwa Chan, Wei-Wen Lin, Yu-Cheng Hsieh, Ming-Chih Lin, Yun-Ching Fu, Tsung Chen, Shih-Ann Chen, Henry Horng-Shing Lu

    Published 2024-01-01
    “…The model achieved impressive performance, with an F1-score of 0.846, an accuracy of 0.909, and an AUC of 0.914 when tested on the Taipei Veterans General Hospital (TVGH) dataset. Additionally, a single-blinding study and external testing using data from the Taichung Veterans General Hospital (TCVGH) demonstrated the reliability and effectiveness of the model, achieving an F1-score of 0.727, an accuracy of 0.806, and an AUC of 0.918, demonstrating the model’s reliability and usefulness. …”
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