Purpose: This study aimed at gaining insight into supportive care needs and cancer treatment-related symptoms, and to determine factors associated with supportive care needs. Breast cancer and its treatment cause emotional trauma and health complaints. These lead to supportive care needs in some patients, while others are more able to cope with these consequences themselves. To be able to address these needs, it is important to identify patients' needs at the time they arise.
Methods: Women (n = 175) with newly-diagnosed breast cancer, under treatment in two Swiss breast cancer clinics, participated in a cross-sectional survey. Standardized instruments were used: Supportive Care Needs Survey, Cancer- and Cancer Treatment-related Symptom Scale, Hospital Anxiety and Depression Scale, Distress Thermometer, and Interpersonal Relationship Inventory.
Results: The patients' most needed help with psychological issues. Many had treatment-related symptoms like fatigue (87.7%), hot flashes (71.5%), and a changed body appearance (55.8%). The majority suffered from distress (56.2%), fewer from anxiety (24.1%) and depression (12.1%). Physical and social impairment, impaired body image, distress, anxiety and depression, a lack of social support and conflicts in their personal relationships were associated with supportive care needs.
Conclusions: The findings can help to identify more vulnerable patients with unmet needs and a higher demand for support. Assessment of patients enables health care professionals to provide support and counselling. In these assessments, the patients' relationship to close relatives should also be addressed.
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http://dx.doi.org/10.1016/j.ejon.2012.02.003 | DOI Listing |
Sci Rep
December 2024
Department of Diagnostic Radiology, Dalhousie University, Halifax, Canada.
The goal of this study was to determine how radiologists' rating of image quality when using 0.5T Magnetic Resonance Imaging (MRI) compares to Computed Tomography (CT) for visualization of pathology and evaluation of specific anatomic regions within the paranasal sinuses. 42 patients with clinical CT scans opted to have a 0.
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December 2024
The School of Nursing, Fujian Medical University, No. 1 Xuefu North Road, Fuzhou, 350122, Fujian, China.
Diabetes Mellitus combined with Mild Cognitive Impairment (DM-MCI) is a high incidence disease among the elderly. Patients with DM-MCI have considerably higher risk of dementia, whose daily self-care and life management (i.e.
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December 2024
Department of Chemistry, University of Washington, Box 351700, Seattle, Washington, 98195, USA.
Trigger valves are fundamental features in capillary-driven microfluidic systems that stop fluid at an abrupt geometric expansion and release fluid when there is flow in an orthogonal channel connected to the valve. The concept was originally demonstrated in closed-channel capillary circuits. We show here that trigger valves can be successfully implemented in open channels.
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December 2024
Research Centre for Biomedical Engineering (RCBE), School of Science and Technology, City, University of London, Northampton Square, London, EC1V 0HB, UK.
Traditional methods for management of mental illnesses in the post-pandemic setting can be inaccessible for many individuals due to a multitude of reasons, including financial stresses and anxieties surrounding face-to-face interventions. The use of a point-of-care tool for self-management of stress levels and mental health status is the natural trajectory towards creating solutions for one of the primary contributors to the global burden of disease. Notably, cortisol is the main stress hormone and a key logical indicator of hypothalamic-pituitary adrenal (HPA) axis activity that governs the activation of the human stress system.
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December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
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