Background: In 2013 the Joint British Diabetes Societies published an update to their 2010 guideline on the management of diabetic ketoacidosis (DKA). In 2014 a national survey was conducted to assess the management of DKA across the UK using the JBDS or local guidelines. Hospitals were invited to submit data on 5 people presenting with DKA. These data were published in 2016. However, whether those national results were applicable to individual hospitals remains unknown.
Aim: To assess the management of people presenting with DKA at a single hospital and compare the results with the national dataset.
Methods: Using the identical data collection tool as used in the national survey we collected information on 40 subjects (a total of 52 admissions) admitted with DKA between April 2014 and July 2015.
Results: The data collected locally were very similar to those found in the national dataset. The management of DKA was best during the first few hours after admission, then biochemical and physical monitoring frequency decreased. The number of people who developed hypokalaemia and hypoglycaemia were very similar to the national data. Rates of biochemical improvement were slightly better locally.
Conclusions: The data from the national DKA survey, even though based on a maximum of 5 people per hospital from across the UK are applicable at a hospital level.
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http://dx.doi.org/10.1016/j.diabres.2017.03.004 | DOI Listing |
Cancer
January 2025
Fox Chase Cancer Center, Temple University Health System, Philadelphia, Pennsylvania, USA.
Background: Little is known about the role that charitable copay assistance (CPA) plays in addressing access to care and financial distress. The study sought to evaluate financial distress and experience with CPA among patients with cancer and autoimmune disease.
Methods: This is a national cross-sectional self-administered anonymous electronic survey conducted among recipients of CPA to cover the costs of a drug for cancer or autoimmune disease.
JAMA Netw Open
January 2025
Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Arch Sex Behav
January 2025
School of Kinesiology and Health Science, 301F Stong College, York University, 4700 Keele St., Toronto, ON, Canada.
Critiques from queer theory have suggested that the legalization of same-sex marriage (SSM) predominantly benefits White, middle-class segments of the lesbian, gay, and bisexual (LGB) community. This study investigates the impact of the legalization of SSM on mental health among Black LGB individuals, focusing on those with lower incomes in the UK. Using a nationally representative panel sample and employing a quasi-experimental methodology, we analyzed changes in psychological distress and life dissatisfaction following the legalization of SSM.
View Article and Find Full Text PDFEur Geriatr Med
January 2025
Department of Cardiovascular Medicine, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, 710004, Shaanxi, People's Republic of China.
Aim: Frailty is an important risk factor for a wide range of chronic diseases and for mortality risk. This study aims to explore the relationship between frailty and incidence of chronic kidney disease (CKD), particularly on the change and accumulation of frailty.
Methods: Frailty status was assessed using the frailty index (FI, constructed by 31 items) and categorized as robust, pre-frail, and frail.
Eur Radiol Exp
January 2025
Computational Clinical Imaging Group (CCIG), Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
Good practices in artificial intelligence (AI) model validation are key for achieving trustworthy AI. Within the cancer imaging domain, attracting the attention of clinical and technical AI enthusiasts, this work discusses current gaps in AI validation strategies, examining existing practices that are common or variable across technical groups (TGs) and clinical groups (CGs). The work is based on a set of structured questions encompassing several AI validation topics, addressed to professionals working in AI for medical imaging.
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