Indices of classification accuracy of the Substance Use/Abuse scale of a Spanish-language version of the Problem Oriented Screening Instrument for Teenagers (POSIT) were evaluated among school-based youth in Mexico. Participants were 1203 youth attending one middle school (N = 619) and one high school (N = 584) in the third largest city of Coahuila, a northern border state in Mexico in May 1998. More than 94% of youth enrolled in the participating middle school and 89% of youth enrolled in the participating high school completed the International Longitudinal Survey of Adolescent Health. Indices of classification accuracy of the POSIT Substance Use/Abuse scale were evaluated against a "drug abuse" problem severity criterion that combined youth meeting DSM-IV criteria for alcohol abuse/dependence disorders with youth having used other illicit drugs five or more times in their lifetime. The present study findings suggest that using a cut score of one or two on the POSIT Substance Use/Abuse scale generally yields optimal classification accuracy indices that vary somewhat by gender and school subgroups. Further, classification accuracy indices of the POSIT Substance Use/Abuse scale are slightly better when used among high school males due, in part, to the higher base rate of serious involvement among this group compared to others.
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Sci Rep
January 2025
Department of ECE, Kallam Haranadhareddy Institute of Technology, Guntur, Andhra Pradesh, India.
Cognitive load stimulates neural activity, essential for understanding the brain's response to stress-inducing stimuli or mental strain. This study examines the feasibility of evaluating cognitive load by extracting, selection, and classifying features from electroencephalogram (EEG) signals. We employed robust local mean decomposition (R-LMD) to decompose EEG data from each channel, recorded over a four-second period, into five modes.
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January 2025
Department of Medicine, Nephrology Division, University of Verona, Verona, Italy.
Introduction: Pain is one of the most frequently reported symptoms in hemodialyzed (HD) patients, with prevalence rates between 33% and 82%. Risk factors for chronic pain in HD patients are older age, long-lasting dialysis history, several concomitant diseases, malnutrition, and others. However, chronic pain assessment in HD patients is rarely performed by specialists in pain medicine, with relevant consequences in terms of diagnostic and treatment accuracy.
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January 2025
Institute for System Dynamics, University of Stuttgart, Waldburgstr. 19, 70563, Stuttgart, Germany.
Including sensor information in medical interventions aims to support surgeons to decide on subsequent action steps by characterizing tissue intraoperatively. With bladder cancer, an important issue is tumor recurrence because of failure to remove the entire tumor. Impedance measurements can help to classify bladder tissue and give the surgeons an indication on how much tissue to remove.
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January 2025
College of Computer and Information Engineering, Nanjing Tech University, Nanjing, 211800, China.
Graph data is essential for modeling complex relationships among entities. Graph Neural Networks (GNNs) have demonstrated effectiveness in processing low-order undirected graph data; however, in complex directed graphs, relationships between nodes extend beyond first-order connections and encompass higher-order relationships. Additionally, the asymmetry introduced by edge directionality further complicates node interactions, presenting greater challenges for extracting node information.
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January 2025
Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, 08193, Spain.
In this study, we explore an enhancement to the U-Net architecture by integrating SK-ResNeXt as the encoder for Land Cover Classification (LCC) tasks using Multispectral Imaging (MSI). SK-ResNeXt introduces cardinality and adaptive kernel sizes, allowing U-Net to better capture multi-scale features and adjust more effectively to variations in spatial resolution, thereby enhancing the model's ability to segment complex land cover types. We evaluate this approach using the Five-Billion-Pixels dataset, composed of 150 large-scale RGB-NIR images and over 5 billion labeled pixels across 24 categories.
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