The purpose of this study was to determine the accuracy of detecting knee effusion with clinical examination and to evaluate whether the amount of effusion, patient obesity, and the clinicians' experience affect the clinicians' decisions in patients with knee osteoarthritis. Patients presenting with knee pain were examined by two residents with different levels of experience and underwent ultrasonographic examination, including measurement of effusion in the medial, mid, and lateral aspects of the suprapatellar bursa. One hundred seventy-two knees of 86 patients were examined. Of the knees investigated, 127 (73.8 %) had effusion. The consistency between ultrasonographic and resident examination were weak (κ = 0.193, p = 0.007 and κ = 0.349, p < 0.001), although the more experienced senior resident had a stronger agreement. The overall inter-rater agreement between the two residents was low (κ = 0.254). The senior resident had a significantly higher accuracy ratio (p = 0.036). In the knees without effusion, the two examiners had no agreement (κ = -0.028, p = 0.856); however, the ratios of the true decisions were similar (p = 1.0). The accuracy of the less experienced resident's decisions was affected by effusion depth (p = 0.005). Clinicians' decisions and their accuracy in detecting knee effusion during clinical examination were different, especially in the absence of effusion. The consistency between ultrasonography and residents was low. The accuracy of clinical examination was affected by effusion depth and experience, but not by patient obesity.
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http://dx.doi.org/10.1007/s10067-013-2356-6 | DOI Listing |
J Occup Health
January 2025
Panasonic Corporation, Department Electric Works Company/Engineering Division, Osaka, Japan.
Background: Falls are among the most prevalent workplace accidents, necessitating thorough screening for susceptibility to falls and customization of individualized fall prevention programs. The aim of this study was to develop and validate a high fall risk prediction model using machine learning (ML) and video-based first three steps in middle-aged workers.
Methods: Train data (n=190, age 54.
J Patient Rep Outcomes
January 2025
Psycho-Oncology Cooperative Research Group, School of Psychology, Faculty of Science, The University of Sydney, Camperdown, NSW, 2006, Australia.
Purpose: Informal caregivers of people with high grade glioma (HGG) often have high levels of unmet support needs. Routine screening for unmet needs can facilitate appropriate and timely access to supportive care. We aimed to develop a brief screening tool for HGG caregiver unmet needs, based on the Supportive Care Needs Survey-Partners & Caregivers (SCNS-P&C).
View Article and Find Full Text PDFVis Comput Ind Biomed Art
January 2025
School of Engineering Medicine and School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.
Fluorescence endoscopy technology utilizes a light source of a specific wavelength to excite the fluorescence signals of biological tissues. This capability is extremely valuable for the early detection and precise diagnosis of pathological changes. Identifying a suitable experimental approach and metric for objectively and quantitatively assessing the imaging quality of fluorescence endoscopy is imperative to enhance the image evaluation criteria of fluorescence imaging technology.
View Article and Find Full Text PDFJ Nephrol
January 2025
Department of Diabetology, Endocrinology, Nephrology, University of Tuebingen, Tuebingen, Germany.
Background: The estimation of glomerular filtration rate (eGFR) is essential in the early detection of diabetic nephropathy. We herein compare the performance of common eGFR formulas against a gold standard measurement of GFR in patients with diabetes mellitus.
Methods: GFR was measured in 93 patients with diabetes mellitus using iohexol clearance as the reference standard.
Eur Radiol
January 2025
Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China.
Objectives: To evaluate the image quality and lung nodule detectability of ultralow-dose CT (ULDCT) with adaptive statistical iterative reconstruction-V (ASiR-V) post-processed using a deep learning image reconstruction (DLIR)-based image domain compared to low-dose CT (LDCT) and ULDCT without DLIR.
Materials And Methods: A total of 210 patients undergoing lung cancer screening underwent LDCT (mean ± SD, 0.81 ± 0.
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