Introduction: About one-third of cancer survivors suffer from severe chronic fatigue. Aim of this study was to evaluate the efficacy of mindfulness-based cognitive group therapy in reducing severe chronic fatigue in cancer survivors with mixed diagnoses.
Patients And Methods: Participants (n = 100) were randomly selected from a cohort and allocated to an intervention and a waiting list condition. Analyses were based on 59 participants in the intervention condition and 24 in the waiting-list condition. Fatigue severity (Checklist Individual Strength), functional impairment (Sickness Impact Profile) and well being (Health and Disease-Inventory) were assessed before and after the 9-week intervention. The intervention group had a follow-up 6 months following the intervention.
Results: At post-treatment measurement the proportion of clinically improved participants was 30%, versus 4% in the waiting list condition (χ(2) (1) = 6.71; p = 0.007). The mean fatigue score at post-measurement was significantly lower in the intervention group than in the waiting list group corrected for pre-treatment level of fatigue. The mean well-being score at post-measurement was significantly higher in the intervention group than in the waiting list group corrected for pre-treatment level of well-being. The treatment effect was maintained at 6-month follow-up. No difference between the two conditions was found in functional impairment.
Discussion: Mindfulness-based cognitive therapy is an effective treatment for chronic cancer-related fatigue.
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http://dx.doi.org/10.1002/pon.1890 | DOI Listing |
Public Health Pract (Oxf)
June 2025
Wolfson Institute of Population Health, Queen Mary University of London, London, UK.
Objectives: Private healthcare is a rapidly growing industry in the UK, particularly for surgical procedures, due to extensive waiting times in publicly funded health care. The NHS also commissions private healthcare to provide procedures for NHS patients to alleviate waiting times. We aimed to explore the trends and geographical variations between the North and South of England in privately funded and NHS-funded privately delivered orthopaedic procedures compared to NHS waiting times.
View Article and Find Full Text PDFBMC Womens Health
January 2025
School of Nursing and Midwifery, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia.
Background: Ovarian cancer is a leading cause of mortality worldwide. The third most prevalent gynecological cancer globally, following cervical and uterine cancer, and the third leading cause of cancer-related mortality among women in Sub-Saharan Africa, including Ethiopia. The time ovarian cancer patients have to wait between diagnosis and initiation of treatment are the indicators of quality in cancer care and influence patient outcomes.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Anesthesiology, Changhua Christian Hospital, Changhua, 50050, Taiwan.
In the modern healthcare system, the rational allocation of emergency department (ED) resources is crucial for enhancing emergency response efficiency, ensuring patient safety, and improving the quality of medical services. This paper focuses on the issue of ED resource allocation and designs a priority sorting system for ED patients. The system classifies patients into two queues: urgent and routine.
View Article and Find Full Text PDFClin Gastroenterol Hepatol
January 2025
Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain. Campus Universitario de Rabanales, Albert Einstein Building. Ctra. N-IV, Km. 396. 14071, Córdoba, Spain; Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, Spain. Av. Menéndez Pidal, s/n, Poniente Sur, 14004 Córdoba, Spain.
Background & Aims: We aimed to develop and validate an artificial intelligence score (GEMA-AI) to predict liver transplant (LT) waiting list outcomes using the same input variables contained in existing models.
Methods: Cohort study including adult LT candidates enlisted in the United Kingdom (2010-2020) for model training and internal validation, and in Australia (1998-2020) for external validation. GEMA-AI combined international normalized ratio, bilirubin, sodium, and the Royal Free Glomerular Filtration Rate in an explainable Artificial Neural Network.
Front Public Health
January 2025
Transplant Immunology Unit, Geneva University Hospitals, Geneva, Switzerland.
Introduction: The Swiss allocation system for kidney transplantation has evolved over time to balance medical urgency, immunological compatibility, and waiting time. Since the introduction of the transplantation law in 2007, which imposed organ allocation on a national level, the algorithm has been optimized. Initially based on waiting time, HLA compatibility, and crossmatch performed by cell complement-dependent cytotoxicity techniques, the system moved in 2012 to a score including HLA compatibility, waiting time, anti-HLA antibodies detected by the Luminex technology, and a virtual crossmatch.
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