Purpose: To evaluate the predictive validity of a screening instrument measuring disability, self-efficacy, fear of movement and catastrophizing, for disability status in patients with musculoskeletal pain in primary health care physical therapy. Development over time of pain-related disability, pain intensity, self-reported work capacity and overall daily function for subgroups of patients was also investigated.
Method: Prospective and correlational study, where patients (n = 168) with a pain-duration of 4 weeks or more completed the questionnaires and their cases were followed for 8 months to assess the variables of interest. For predictive validity of the screening instrument discriminant analyses were conducted. The development over time for subgroups was analysed by comparing scores at the first and second measurement.
Results: The PBSI correctly classified 72% of the subjects as High-disabled (n = 33) or Low-disabled (n = 110), as measured with the Pain Disability Index (Wilks' lambda = 0.848, p < 0.005). For pain intensity, self-reported changes in work capacity and overall daily function the discriminant analyses were not significant. The High-disability group had increased disability, unchanged pain intensity and decreased work capacity and daily function after 8 months.
Conclusion: The predictive validity of the PBSI for disability was confirmed. In clinical use the PBSI could serve as a mean to obtain supplementary and clinically useful information.
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http://dx.doi.org/10.1080/09638280701523200 | DOI Listing |
Psychiatry Clin Psychopharmacol
December 2024
The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Background: This study aimed to investigate miRNAs and upstream regulatory transcription factors involved in schizophrenia (SZ) pathogenesis.
Methods: Differential expression of miRNAs and genes in SZ patients was investigated utilizing the gene expression omnibus dataset, gene ontology annotations, and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. Real-time quantitative polymerase chain reaction experiments were conducted to validate the predictive screening of regulatory genes in peripheral blood samples from 20 SZ patients and 20 healthy controls.
Sensors (Basel)
December 2024
School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450000, China.
To address the limitations of existing deep learning-based algorithms in detecting surface defects on brake pipe ends, a novel lightweight detection algorithm, FP-YOLOv8, is proposed. This algorithm is developed based on the YOLOv8n framework with the aim of improving accuracy and model lightweight design. First, the C2f_GhostV2 module has been designed to replace the original C2f module.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
Freezing of gait (FOG) is a walking disturbance that can lead to postural instability, falling, and decreased mobility in people with Parkinson's disease. This research used machine learning to predict and detect FOG episodes from plantar-pressure data and compared the performance of decision tree ensemble classifiers when trained on three different datasets. Dataset 1 ( = 11) was collected in a previous study.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Mechanical Engineering, Ming Chi University of Technology, New Taipei City 24301, Taiwan.
Unbalance faults are among the common causes of interruptions and unexpected failures in rotary systems. Therefore, monitoring unbalance faults is essential for predictive maintenance. While conventional time-invariant mathematical models can assess the impact of these faults, they often rely on proper assumptions of system factors like bearing stiffness and damping characteristics.
View Article and Find Full Text PDFSensors (Basel)
December 2024
CNRS, LAAS, 7 Avenue du Colonel Roche, F-31400 Toulouse, France.
The development of ion-sensitive field-effect transistor (ISFET) sensors based on silicon nanowires (SiNW) has recently seen significant progress, due to their many advantages such as compact size, low cost, robustness and real-time portability. However, little work has been done to predict the performance of SiNW-ISFET sensors. The present study focuses on predicting the performance of the silicon nanowire (SiNW)-based ISFET sensor using four machine learning techniques, namely multilayer perceptron (MLP), nonlinear regression (NLR), support vector regression (SVR) and extra tree regression (ETR).
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