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Background: Thyroid nodules classified cytologically as low-risk indeterminate lesions (TIR3A) on fine-needle aspiration biopsy (FNAB) present a clinical challenge due to their uncertain malignancy risk. This single-center study aimed to evaluate the natural history of TIR3A nodules.

Materials And Methods: FNABs performed between July 2017 and December 2019 were retrospectively retrieved and patients with TIR3A nodules were evaluated at baseline and throughout a follow-up based on ultrasound (US) parameters and clinical data.

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Job-exposure matrix (JEM) validity on crystalline silica among systemic sclerosis patients.

Occup Med (Lond)

January 2025

Maine et Loire, Univ Angers, CHU Angers, Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, IRSET-ESTER, SFR ICAT, CAPTV CDC, 49000 Angers, France.

Background: Systemic sclerosis (SSc) is the connective tissue disease with the highest individual mortality. Crystalline silica is known to be an occupational risk factor for SSc. To assess past crystalline silica exposure, we aimed to study the validity of a job exposure matrix (JEM) to assess occupational exposure to crystalline silica compared to specific occupational interviews in two populations of SSc patients.

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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.

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Background: TheKeep.Ca was built to facilitate engagement with those experiencing cancer in Manitoba, Canada. Constructed between 2020 and 2024 with a group of patient advisors, the website includes information on engagement activities including research participation, the patient advisor role, and how those experiencing cancer can access these Manitoba activities.

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Exploring 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.

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