Objective: The study objective was to assess the awareness of standard precautions (SP) among healthcare professionals, 1 year after the latest national guidelines were issued.
Methods: A multicenter cross-sectional survey was conducted in 34 volunteer institutions in 2010. Data was collected using an anonymous and self-administered questionnaire. The data was analyzed with a program developed from Epi-Info software.
Results: Four thousand four hundred and thirty-nine questionnaires were analyzed. Most respondents were nurses (44.1%) or nurses' aides (26.7%) followed by physicians (3.5%). 25% of respondents had participated in specific PS training in the previous 5 years. The percentage of correct answers for each question ranged from 37.1 to 91%. There was 72.6% of correct answers on hand hygiene but only 7.3% of correct answers on use of appropriate barriers and disposal of needles. 39.3% of respondents gave correct answers to eight or more of the 10 SP questions. The level of knowledge of nurses was higher compared to other professionals. The lowest level of knowledge was observed in long-term care and psychiatric institutions.
Conclusions: The knowledge of healthcare professionals on use of appropriate protective barriers and disposal of needles is still too limited. The survey results should be used to develop adequate and targeted educational interventions.
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http://dx.doi.org/10.1016/j.medmal.2012.11.004 | DOI Listing |
Eur Heart J Digit Health
January 2025
Cardiovascular Center, Tufts Medical Center, 800 Washington Street, Boston, MA 02111, USA.
Aims: This study evaluates the performance of OpenAI's latest large language model (LLM), Chat Generative Pre-trained Transformer-4o, on the Adult Clinical Cardiology Self-Assessment Program (ACCSAP).
Methods And Results: Chat Generative Pre-trained Transformer-4o was tested on 639 ACCSAP questions, excluding 45 questions containing video clips, resulting in 594 questions for analysis. The questions included a mix of text-based and static image-based [electrocardiogram (ECG), angiogram, computed tomography (CT) scan, and echocardiogram] formats.
Cureus
December 2024
Department of Rehabilitation Medicine, Mie University Graduate School of Medicine, Tsu, JPN.
Background Generative artificial intelligence (AI), such as Chat Generative Pre-trained Transformer (ChatGPT), has shown potential in various medical applications, including answering licensing examination questions. However, its performance in rehabilitation medicine remains underexplored. This study aimed to evaluate the accuracy of ChatGPT4o in answering questions from the Japanese Board-Certified Physiatrist Examination and assess its potential as an educational and clinical support tool.
View Article and Find Full Text PDFCureus
December 2024
Internal Medicine, Ross University School of Medicine, Saint Michael, BRB.
Purpose: The integration of artificial intelligence (AI) into medical education has witnessed significant progress, particularly in the domain of language models. This study focuses on assessing the performance of two notable language models, ChatGPT and BingAI Precise, in answering the National Eligibility Entrance Test for Postgraduates (NEET-PG)-style practice questions, simulating medical exam formats.
Methods: A cross-sectional study conducted in June 2023 involved assessing ChatGPT and BingAI Precise using three sets of NEET-PG practice exams, comprising 200 questions each.
Eye (Lond)
January 2025
Ophthalmology Department, Norfolk & Norwich University Hospital, Norwich, UK.
Background: This study presents a comprehensive evaluation of the performance of various large language models in generating responses for ophthalmology emergencies and compares their accuracy with the established United Kingdom's National Health Service 111 online system.
Methods: We included 21 ophthalmology-related emergency scenario questions from the NHS 111 triaging algorithm. These questions were based on four different ophthalmology emergency themes as laid out in the NHS 111 algorithm.
Front Oncol
January 2025
Department of Radiology, Ordos Central Hospital, Ordos, Inner Mongolia, China.
Background: Improvements in the clinical diagnostic use of magnetic resonance imaging (MRI) for the identification of liver disorders have been made possible by gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA). Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) technology is in high demand.
Objectives: The purpose of the study is to segment the liver using an enhanced multi-gradient deep convolution neural network (EMGDCNN) and to identify and categorize a localized liver lesion using a Gd-EOB-DTPA-enhanced MRI.
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