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| http://dx.doi.org/10.1016/j.resplu.2024.100766 | DOI Listing |
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COVID-19 and influenza vaccine Hesitancy among adults hospitalized in the United States, 2019-2022.
Vaccine
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Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Background: Understanding similarities and differences between hesitancy for influenza and COVID-19 vaccines could facilitate strategies to improve public receptivity toward vaccination.
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Common issues of data science on the eco-environmental risks of emerging contaminants.
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Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education), Carbon Neutrality Interdisciplinary Science Centre, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
Data-driven approaches (e.g., machine learning) are increasingly used to replace or assist laboratory studies in the study of emerging contaminants (ECs).
View Article and Find Full Text PDFExperiences and perspectives of patients undergoing colorectal cancer surgery: A qualitative study.
Eur J Oncol Nurs
January 2025
School of Health and Medical Sciences, City St George's, University of London, London, EC1V 0HB, United Kingdom. Electronic address:
Purpose: This study aimed to explore the experiences of patients with colorectal cancer undergoing surgical treatment, capturing their perspectives from diagnosis through to post-discharge recovery.
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Oral Cyclophosphamide for Patients With Metastatic Castration-Resistant Prostate Cancer in a Scenario of Limited Health Care Resources.
JCO Glob Oncol
January 2025
Genitourinary Medical Oncology Service, Instituto do Câncer do Estado de São Paulo (ICESP), University of São Paulo, São Paulo, Brazil.
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Development and Validation of Machine Learning Models for Risk Prediction of Postpartum Stress Urinary Incontinence: A Prospective Observational Study.
Int Urogynecol J
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School of Nursing, Binzhou Medical University, Bincheng District, No. 522, Huanghe Third Road, Binzhou, Shandong, China.
Introduction And Hypothesis: This study aims to develop a postpartum stress urinary incontinence (PPSUI) risk prediction model based on an updated definition of PPSUI, using machine learning algorithms. The goal is to identify the best model for early clinical screening to improve screening accuracy and optimize clinical management strategies.
Methods: This prospective study collected data from 1208 postpartum women, with the dataset randomly divided into training and testing sets (8:2).
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