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| http://dx.doi.org/10.1016/j.jeconom.2023.105516 | DOI Listing |
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Development, Measurement Properties and Reference Values of the Upper Extremity Motor Coordination Test: A New Motor Coordination Test of the Upper Limbs.
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Department of Physiotherapy, Centro Universitário FUNCESI, Itabira, Brazil.
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Methods: The Upper Extremity Motor Coordination Test (UEMOCOT)was applied for 20 s, with the individual touching two targets (one right and one left) as quickly as possible, first with the hand (manual task) and then with the index finger (finger task). To test-retest reliability, the UEMOCOT was administered two times.
Exploring Gender-Based Disparities in Finalist Technical-Tactical Actions: A Study of Amateur and Professional Padel Players.
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Faculty of Sport Sciences, University of Extremadura, Cáceres, Spain.
Our aim in this study was to analyze gender differences across amateur and professional padel players related to the last shot of each rally. We analyzed a total of 180 games per category (professional and amateur), with 90 games for each gender. We performed descriptive and inferential analyses, including the Chi-square (χ2) statistical test, the Cramer's V coefficient (Vc), subsequent Z-tests and corrected standard residuals (CSR).
View Article and Find Full Text PDFSexual Health Advocacy for Guys (SHAG): a randomized trial of the impact of a text-messaging program on HIV incidence and STI testing among a national sample of sexual minority cisgender adolescent and young adult men.
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View Article and Find Full Text PDFThe influence of electronic health record design on usability and medication safety: systematic review.
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View Article and Find Full Text PDFAutomatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN model.
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Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression levels. While immunohistochemical techniques (IHC) are the gold standard for HER2 evaluation, their implementation can be resource-intensive and costly. To reduce these obstacles and expedite the procedure, we present an efficient deep-learning model that generates high-quality IHC-stained images directly from Hematoxylin and Eosin (H&E) stained images.
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