The use of bedside ultrasound over the past few decades has created a new wave of options for visualizing pathological processes allowing for faster and better detection of disease. We aimed to evaluate the reliability of focused cardiac ultrasound (FCU) performed by first-year internal medicine residents at a community hospital after a short period of training. They received a two-hour lecture and initially performed a supervised FCU followed by ten unsupervised/independent FCUs each. The four parameters that were assessed were left systolic ventricular function, right systolic ventricular function, presence of pericardial effusion, and presence of IVC dilation. Interpretation and analysis of ultrasound images were then carried out by both the residents and an attending physician with expertise in FCU analysis and interpretation. Cohen's Kappa values were obtained comparing the results found by the interns versus the attending. Our findings indicate that more training is required for reliable analysis of FCU by first-year medical residents. Our results also emphasize the need to carefully evaluate the medical residents' FCU skills after the training.
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http://dx.doi.org/10.1080/20009666.2019.1659666 | DOI Listing |
Biomater Transl
November 2024
Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region, China.
The convergence of organoid technology and artificial intelligence (AI) is poised to revolutionise oral healthcare. Organoids - three-dimensional structures derived from human tissues - offer invaluable insights into the complex biology of diseases, allowing researchers to effectively study disease mechanisms and test therapeutic interventions in environments that closely mimic in vivo conditions. In this review, we first present the historical development of organoids and delve into the current types of oral organoids, focusing on their use in disease models, regeneration and microbiome intervention.
View Article and Find Full Text PDFCureus
January 2025
Medical Oncology, Kartal Dr. Lütfi Kirdar City Hospital, Health Science University, Istanbul, TUR.
Integrating artificial intelligence (AI) into oncology can revolutionize decision-making by providing accurate information. This study evaluates the performance of ChatGPT-4o (OpenAI, San Francisco, CA) Oncology Expert, in addressing open-ended clinical oncology questions. Thirty-seven treatment-related questions on solid organ tumors were selected from a hematology-oncology textbook.
View Article and Find Full Text PDFFront Endocrinol (Lausanne)
January 2025
Department of Urology, Harbin Medical University Cancer Hospital, Harbin, China.
Aim: Previous research has shown a strong association between insulin resistance (IR) and both the onset and advancement of diabetic kidney disease (DKD). This research focuses on examining the relationship between IR and all-cause mortality in individuals with DKD.
Methods: This study utilized data obtained from the National Health and Nutrition Examination Survey (NHANES), spanning the years 2001 to 2018.
Integr Pharm Res Pract
January 2025
Facultad de Ciencias de la Salud, Universidad Internacional de Valencia, Valencia, España.
Background: In the realm of Evidence-Based Medicine, introduced by Gordon Guyatt in the early 1990s, the integration of machine learning technologies marks a significant advancement towards more objective, evidence-driven healthcare. Evidence-Based Medicine principles focus on using the best available scientific evidence for clinical decision-making, enhancing healthcare quality and consistency by integrating this evidence with clinician expertise and patient values. Patient-Reported Outcome Measures (PROMs) and Patient-Reported Experience Measures (PREMs) have become essential in evaluating the broader impacts of treatments, especially for chronic conditions like HIV, reflecting patient health and well-being comprehensively.
View Article and Find Full Text PDFInt J Public Health
January 2025
Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
Objectives: To conduct mental health surveillance in adults in Ukraine and Ukrainian refugees (Canton of Zurich, Switzerland) as an actionable scientific foundation for public mental health and mental healthcare.
Methods: Mental Health Assessment of the Population (MAP) is a research program including prospective, population-based, digital cohort studies focused on mental health monitoring. The study aims to include 17,400 people from the general population of Ukraine, 1,220 Ukrainians with refugee status S residing in the canton of Zurich, and 1,740 people from the Zurich general population.
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