The prevalence of osteoporosis is about 40%-50% in postmenopausal women and 20% in older men. The limited availability of dual-energy X-ray absorptiometry (DXA) scanners across the country calls for the presence of alternate risk assessment tools to identify those at high risk for osteoporosis. Some of the screening tools available for osteoporosis include Simple Calculated Osteoporosis Risk Estimation and Osteoporosis Risk Assessment Tool for Asians (OSTA), and Fracture Risk Assessment Tool to assess fracture risk. Clinical parameters that may serve as surrogates include dentition and anthropometric indices. Although screening tools do not supplant the assessment of bone mineral density by DXA, they help identify individuals at high risk for osteoporosis who may be selectively referred for confirming the same.
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http://dx.doi.org/10.4103/jmh.jmh_216_21 | DOI Listing |
Eur J Pediatr
January 2025
Department of Maternal Infantile and Urological Sciences, Sapienza University of Rome, Rome, Italy.
Unlabelled: Klinefelter syndrome (KS) is the most common sex chromosomal aneuploidy in males (47,XXY karyotype in 80-90% of cases), primarily characterized by hypergonadotropic hypogonadism and infertility. It encompasses a broad phenotypic spectrum, leading to variability in neurocognitive and psychosocial outcomes among affected individuals. Despite the recognized correlation between KS and various neuropsychiatric conditions, studies investigating potential sleep disorders, particularly in pediatric subjects, are lacking.
View Article and Find Full Text PDFCurr Cardiol Rep
January 2025
Hasselt University, Faculty of Medicine and Life Sciences / Limburg Clinical Research Centre, Agoralaan, Diepenbeek, Belgium.
Purpose Of Review: This review aims to explore the complex interplay between atrial functional mitral regurgitation (AFMR), atrial fibrillation (AF), and heart failure with preserved ejection fraction (HFpEF). The goal is to define these conditions, examine their underlying mechanisms, and discuss treatment perspectives, particularly addressing diagnostic challenges.
Recent Findings: Recent research highlights the rising prevalence of AFMR, now accounting for nearly one-third of significant mitral regurgitation cases.
BMJ Evid Based Med
December 2024
Department of Public Health, History of Science, and Gynecology, Miguel Hernandez University of Elche Faculty of Medicine, Sant Joan D'Alacant, Comunidad Valenciana, Spain
Objective: The objective of this study is to analyse the perspectives of screening candidates and healthcare professionals on shared decision-making (SDM) in prostate cancer (PCa) screening using the prostate-specific antigen (PSA) test.
Design: Descriptive qualitative study (May-December 2022): six face-to-face focus groups and four semistructured interviews were conducted, transcribed verbatim and thematically analysed using ATLAS.ti software.
Expert Opin Drug Saf
January 2025
Department of Endocrinology, Guang'anmen Hospital of China Academy of Chinese Medical Sciences, Beijing, China.
Background: Fulminant type 1 diabetes mellitus (FT1DM) is a severe subtype of type 1 diabetes characterized by rapid onset, metabolic disturbances, and irreversible insulin secretion failure. Recent studies have suggested associations between FT1DM and certain medications, warranting further investigation.
Objectives: This study aims to analyze drugs associated with an increased risk of FT1DM using the Food and Drug Administration Adverse Event Reporting System (FAERS) database.
J Clin Med
January 2025
Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy.
Sepsis is one of the leading causes of mortality in hospital settings, and early diagnosis is a crucial challenge to improve clinical outcomes. Artificial intelligence (AI) is emerging as a valuable resource to address this challenge, with numerous investigations exploring its application to predict and diagnose sepsis early, as well as personalizing its treatment. Machine learning (ML) models are able to use clinical data collected from hospital Electronic Health Records or continuous monitoring to predict patients at risk of sepsis hours before the onset of symptoms.
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