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Unveiling the relation between swallowing muscle mass and skeletal muscle mass in head and neck cancer patients.

Eur Arch Otorhinolaryngol

January 2025

Department of Otorhinolaryngology, Head and Neck Surgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, PO box 30.001, Groningen, 9700RB, The Netherlands.

Purpose: Sarcopenia, characterized by loss of skeletal muscle mass (SMM) and strength, often leads to dysphagia in the elderly. This condition can also worsen treatment outcomes in head and neck cancer (HNC) patients, who are susceptible to swallowing difficulties. This study aimed to establish the correlation between swallowing muscle mass (SwMM) and SMM in HNC patients.

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[The characteristics and associated factors of hand dysfunction in patients with rheumatoid arthritis].

Zhonghua Nei Ke Za Zhi

February 2025

Department of Rheumatology and Immunology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou510120, China.

To investigate the characteristics of hand dysfunction and its associated factors in patients with rheumatoid arthritis (RA). A cross-sectional study. Patients with RA were recruited from January 2019 to April 2024 at the Department of Rheumatology and Immunology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University.

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Objective: To explore how women who used substances during the perinatal period perceived the care they received from interprofessional perinatal care providers.

Design: Appreciative inquiry.

Setting: Interprofessional perinatal care clinic in a large urban tertiary care hospital in Canada.

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Objective: To analyze the relationship between body fat, motor skills, and physical fitness in children and adolescents.

Methods: 216 children and adolescents (143 males and 73 females, aged 5-15 years) from a social project composed this study. Body mass and height were measured to calculate the body mass index (BMI).

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ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis.

J Biomed Inform

January 2025

Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, 02115, MA, USA; VA Boston Healthcare System, 150 S Huntington Ave, Boston, 02130, MA, USA. Electronic address:

Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. To address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features.

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