Showing 841 - 860 results of 11,932 for search '"They Live"', query time: 0.08s Refine Results
  1. 841

    Hip Arthroplasty in an Acute-On-Chronic Liver Failure Patient Followed by Early Live Donor Liver Transplant by S. Aravind, Jai Thilak, Arun Valsan, Mathew George, Koushik Gundavalli, Shine Sadasivan, S. Sudhindran

    Published 2024-12-01
    “…Addressing each of these risk factors, the patient underwent a cemented bipolar hemiarthroplasty under regional nerve block, followed by early live donor liver transplantation with an uneventful post-op recovery and currently mobilizing without support. …”
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  15. 855

    Farm Cultural Park as an urban regeneration lab by Fausta Occhipinti

    Published 2017-06-01
    Subjects: “…contemporary living…”
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    Indigenous university students' perceptions regarding nature, their daily lives and climate change: a photovoice study by Ieda M. A. V. Dias, Antonio Jose Grande, Paulo T. C. Jardim, Alessandra Aparecida Vieira Machado, Jacks Soratto, Maria Inês da Rosa, Luciane Bisognin Ceretta, Leonardo Roever, Xanthi Zourntos, Seeromanie Harding

    Published 2025-01-01
    “…Abstract Background Climate change has severe health impacts, particularly for populations living in environmentally sensitive areas such as riversides, slopes, and forests. …”
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    Developing a Prototype Machine Learning Model to Predict Quality of Life Measures in People Living With HIV by Mercadal-Orfila G, Serrano López de las Hazas J, Riera-Jaume M, Herrera-Perez S

    Published 2025-01-01
    “…Patient-Reported Outcome Measures (PROMs) and Patient-Reported Experience Measures (PREMs) have become essential in evaluating the broader impacts of treatments, especially for chronic conditions like HIV, reflecting patient health and well-being comprehensively.Purpose: The study aims to leverage Machine Learning (ML) technologies to predict health outcomes from PROMs/PREMs data, focusing on people living with HIV.Patients and Methods: Our research utilizes a ML Random Forest Regression to analyze PROMs/PREMs data collected from over 1200 people living with HIV through the NAVETA telemedicine system.Results: The findings demonstrate the potential of ML algorithms to provide precise and consistent predictions of health outcomes, indicating high reliability and effectiveness in clinical settings. …”
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