Objective: To explore indicators that predict whether patients with extremity pain have a spinal or extremity source of pain.
Methods: The data were from a prospective cohort study (n = 369). Potential indicators were gathered from a typical Mechanical Diagnosis and Therapy (MDT) history and examination. A stepwise logistic regression with a backward elimination was performed to determine which indicators predict classification into spinal or extremity source groups. A Receiver Operating Characteristic (ROC) curve was constructed to examine the number of significant indicators that could predict group classification.
Results: Five indicators were identified to predict group classification. Classification into the spinal group was associated with the presence of paresthesia [odds ratio (OR) 1.984], change in symptoms with sitting/neck or trunk flexion/turning neck/when still (OR 2.642), change in symptoms with posture change (OR 3.956), restrictions in spinal movements (OR 2.633), and no restrictions in extremity movements (OR 2.241). The optimal number of indicators for classification was two (sensitivity = 0.638, specificity = 0.807).
Discussion: This study provides guidance on clinical indicators that predict the source of symptoms for isolated extremity pain. The clinical indicators will allow clinicians to supplement their decision-making process in regard to spinal and extremity differentiation so as to appropriately target their examinations and interventions.
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http://dx.doi.org/10.1080/10669817.2022.2030625 | DOI Listing |
Curr Neurovasc Res
January 2025
Department of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province, China.
Background: Plasma osteoprotegerin (OPG) has been linked to poor prognosis following stroke, but its impact on post-stroke cognitive impairment (PSCI) is unknown. The purpose of our work was to analyze the relationship of OPG with PSCI.
Methods: Our study included 613 ischemic stroke subjects with plasma OPG levels.
Front Oncol
January 2025
Department of Gastric and Colorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, Jilin, China.
Background: Colorectal cancer (CRC) is a common malignancy with notable recent shifts in its burden distribution. Current data on CRC burden can guide screening, early detection, and treatment strategies for efficient resource allocation.
Methods: This study utilized data from the latest Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study.
Theranostics
January 2025
Department of Physiology & Medical Physics, RCSI University of Medicine & Health Sciences, Dublin D02 YN77, Ireland.
Post-traumatic epilepsy (PTE) is one of the most common life-quality reducing consequences of traumatic brain injury (TBI). However, to date there are no pharmacological approaches to predict or to prevent the development of PTE. The P2X7 receptor (P2X7R) is a cationic ATP-dependent membrane channel that is expressed throughout the brain.
View Article and Find Full Text PDFF1000Res
January 2025
Dept. Computer Science, Integrative Bioinformatics, Vrije Universiteit, Amsterdam, The Netherlands.
The solute carrier (SLC) family of membrane proteins is a large class of transporters for many small molecules that are vital for cellular function. Several pathogenic mutations are reported in the glucose transporter subfamily SLC2, causing Glut1-deficiency syndrome (GLUT1DS1, GLUT1DS2), epilepsy (EIG2) and cryohydrocytosis with neurological defects (Dystonia-9). Understanding the link between these mutations and transporter dynamics is crucial to elucidate their role in the dysfunction of the underlying transport mechanism, which we investigate using molecular dynamics simulations.
View Article and Find Full Text PDFJACC Asia
January 2025
Department of Frontier Cardiovascular Science, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Background: Heart failure should be diagnosed as early as possible. Although deep learning models can predict one or more echocardiographic findings from electrocardiograms (ECGs), such analyses are not comprehensive.
Objectives: This study aimed to develop a deep learning model for comprehensive prediction of echocardiographic findings from ECGs.
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