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A Randomized, Controlled Trial of In-Hospital Use of Virtual Reality to Reduce Preoperative Anxiety Prior to Cardiac Surgery.

Mayo Clin Proc

January 2025

Departments of Cardiovascular Surgery, Mayo Clinic, Rochester, MN, USA; Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA; Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA. Electronic address:

Objective: To study the effectiveness of virtual reality (VR) in reducing anxiety levels in patients undergoing first-time sternotomy for cardiac surgery.

Patients And Methods: A total of 100 adult patients scheduled for cardiac surgery at Mayo Clinic in Rochester, Minnesota, USA, was recruited from April 19, 2022, to October 12, 2022. Before surgery, patients wore a physiological monitor to record vital signs.

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Aim: To identify the barriers and enablers in the implementation of evidence-based physical activity (PA) programmes for the improvement of health outcomes among pregnant women with gestational diabetes mellitus (GDM), and to develop strategies for implementing this evidence in clinical practice.

Methods: A convergent mixed-methods study was conducted, integrating a descriptive qualitative research design with a cross-sectional survey. In-depth interview was used to collect the views and cognitions about physical activity from medical staff, leaders and pregnant women.

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A Novel Team-Based Approach to Oral Diagnosis Education: Case of the Week.

Eur J Dent Educ

January 2025

Department of Orthodontics and Pediatric and Community Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, UAE.

Background: Traditional oral diagnosis education often relies on passive lectures and individual case assessments. Team-based learning (TBL) offers an interactive alternative, but implementation challenges can exist. The 'Case of the Week (COW)' method presents a potentially modified TBL approach for oral diagnosis education.

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Background: Cancer requires interdisciplinary intersectoral care. The Care Coordination Instrument (CCI) captures patients' perspectives on cancer care coordination. We aimed to translate, adapt, and validate the CCI for Germany (CCI German version).

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A machine learning-based model to predict POD24 in follicular lymphoma: a study by the Chinese workshop on follicular lymphoma.

Biomark Res

January 2025

Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, P.R. China.

Background: Disease progression within 24 months (POD24) significantly impacts overall survival (OS) in patients with follicular lymphoma (FL). This study aimed to develop a robust predictive model, FLIPI-C, using a machine learning approach to identify FL patients at high risk of POD24.

Methods: A cohort of 1,938 FL patients (FL1-3a) from seventeen centers nationwide in China was randomly divided into training and internal validation sets (2:1 ratio).

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