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http://dx.doi.org/10.1016/j.injury.2020.06.049 | DOI Listing |
Basic Clin Androl
January 2025
Department of Obstetrics and Gynaecology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, Hong Kong.
Background: Manual counting for semen analysis is recommended by the World Health Organization. Technicians performing this usually record their results on a paper worksheet and then enter the data into an electronic laboratory information system. One disadvantage of this approach is the chance of post-analytical transcription errors, which can be reduced by checking the computer entries before reporting by another technician.
View Article and Find Full Text PDFPharmacoeconomics
January 2025
Belgian Health Care Knowledge Centre, Brussels, Belgium.
Background: Forecasting future public pharmaceutical expenditure is a challenge for healthcare payers, particularly owing to the unpredictability of new market introductions and their economic impact. No best-practice forecasting methods have been established so far. The literature distinguishes between the top-down approach, based on historical trends, and the bottom-up approach, using a combination of historical and horizon scanning data.
View Article and Find Full Text PDFDrug Saf
January 2025
Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Background: Natural language processing (NLP) and machine learning (ML) techniques may help harness unstructured free-text electronic health record (EHR) data to detect adverse drug events (ADEs) and thus improve pharmacovigilance. However, evidence of their real-world effectiveness remains unclear.
Objective: To summarise the evidence on the effectiveness of NLP/ML in detecting ADEs from unstructured EHR data and ultimately improve pharmacovigilance in comparison to other data sources.
Purpose: Caregivers in pediatric oncology need accurate and understandable information about their child's condition, treatment, and side effects. This study assesses the performance of publicly accessible large language model (LLM)-supported tools in providing valuable and reliable information to caregivers of children with cancer.
Methods: In this cross-sectional study, we evaluated the performance of the four LLM-supported tools-ChatGPT (GPT-4), Google Bard (Gemini Pro), Microsoft Bing Chat, and Google SGE-against a set of frequently asked questions (FAQs) derived from the Children's Oncology Group Family Handbook and expert input (In total, 26 FAQs and 104 generated responses).
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