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Background: Sarcopenia is closely associated with a poor quality of life and mortality, and its prevention and treatment represent a critical area of research. Resistance training is an effective treatment for older adults with sarcopenia. However, they often face challenges when receiving traditional rehabilitation treatments at hospitals.

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Background: Ischemic heart disease (IHD) has a significant impact on public health and healthcare expenditures in the United States (US).

Methods: We used data from the CDC WONDER database from 1999-2020 to identify trends in the IHD-related mortality of patients ≥ 75 years in the US. AAMRs per 100,000 population and APC were calculated and categorized by year, sex, race, and geographic divisions.

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German cockroaches (Blattella germanica (L.)) are a persistent pest in affordable housing and studies indicate that residents implement control on their own to deal with cockroaches within their homes. While many do-it-yourself (DIY) control options have proven ineffective, baits are widely considered to be a viable DIY solution for residents who do not have access to professional pest control services.

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Cost-effectiveness of data driven personalised antibiotic dosing in critically ill patients with sepsis or septic shock.

J Clin Monit Comput

January 2025

Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Public Health research institute, Van der Boechorststraat 7, Amsterdam, 1081 BT, the Netherlands.

Purpose: This study provides an economic evaluation of bedside, data-driven, and model-informed precision dosing of antibiotics in comparison with usual care among critically ill patients with sepsis or septic shock.

Methods: This economic evaluation was conducted alongside an AutoKinetics randomized controlled trial. Effect measures included quality-adjusted life years (QALYs), mortality and pharmacokinetic target attainment.

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Background: The discharge practices from the intensive care unit exhibit heterogeneity and the recognition of eligible patients for discharge is often delayed. Recognizing the importance of safe discharge, which aims to minimize readmission and mortality, we developed a dynamic machine-learning model. The model aims to accurately identify patients ready for discharge, offering a comparison of its effectiveness with physician decisions in terms of safety and discrepancies in discharge readiness assessment.

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