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Background: The purpose of this study was to evaluate the performance and evolution of Chat Generative Pre-Trained Transformer (ChatGPT; OpenAI) as a resource for shoulder and elbow surgery information by assessing its accuracy on the American Academy of Orthopaedic Surgeons shoulder-elbow self-assessment questions. We hypothesized that both ChatGPT models would demonstrate proficiency and that there would be significant improvement with progressive iterations.
Materials And Methods: A total of 200 questions were selected from the 2019 and 2021 American Academy of Orthopaedic Surgeons shoulder-elbow self-assessment questions.
Analysis of the Top Articles and Authors of the Journal of Orthopaedic Trauma and Bibliometric Comparison of Impact Factors of Orthopaedic Surgery Journals.
J Orthop Trauma
January 2025
Department of Orthopaedic Surgery, Medical City Denton, 3535 S Interstate 35, Denton, TX 76210, United States.
Objectives: To determine the top 100 cited authors and the top 20 articles in the Journal of Orthopaedic Trauma (JOT) and compare its impact factor to orthopaedic and non-orthopaedic surgery literature.
Design: Review.
Methods: The Web of Science database was used to determine the top 100 cited authors and top 20 cited articles that originated in JOT from 1995 to the present.
Transformers for Neuroimage Segmentation: Scoping Review.
J Med Internet Res
January 2025
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation.
View Article and Find Full Text PDFExploring the Credibility of Large Language Models for Mental Health Support: Protocol for a Scoping Review.
JMIR Res Protoc
January 2025
Data and Web Science Group, School of Business Informatics and Mathematics, University of Manneim, Mannheim, Germany.
Background: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. However, the credibility of LLMs in providing reliable and explainable mental health information and support remains underexplored.
View Article and Find Full Text PDFNanoparticle encapsulation enables systemic IGF-Trap delivery to inhibit intra-cerebral glioma growth.
Neuro Oncol
January 2025
Department of Medicine, Division of Experimental Medicine, McGill University.
Background: Glioblastoma is an aggressive brain cancer with a 5-year survival rate of 5-10%. Current therapeutic options are limited, due in part to drug exclusion by the blood-brain barrier, restricting access of targeted drugs to the tumor. The receptor for the type 1 insulin-like growth factor (IGF-1R) was identified as a therapeutic target in glioblastoma.
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