Liangyu Yin

1 Introduction The capacity to maintain adequate physical functioning and mobility independence is crucial for lifelong health [1-3]. Ageing is a leading cause of functional limitations, which are linked to an increased risk of disability, cognitive impairment, institutionalization and early mortality [4-6]. Alarmingly, the global ageing population is projected to reach 1.5 billion by 2050 [7].

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button. Persistent malnutrition is associated with poor clinical outcomes in cancer. However, assessing its reversibility can be challenging. The present study aimed to utilize machine learning (ML) to predict reversible malnutrition (RM) in patients with cancer. A multicenter cohort study including hospitalized oncology patients.

Introduction The importance of body size on cancer prognosis is a topic of growing medical concerns worldwide.1, 2 Body mass index (BMI) is the most commonly used measure of body size, and its prognostic role in cancer has been extensively investigated.3-7 However, the results are still subject to debate: While high BMI is linked to poorer survival in breast cancer,8 it may improve survival in lung,4 gastric5 and colon cancers.7 The ‘obesity paradox’ in oncology populations also adds...