The purpose of this study was to describe and evaluate a newly developed on-ice timing system for team evaluation in the sport of ice hockey. We hypothesized that this new, simple, inexpensive, timing system would prove to be highly accurate and reliable. Six adult subjects (age 30.4 ± 6.2 years) performed on ice tests of acceleration and conditioning. The performance times of the subjects were recorded using a handheld stopwatch, photocell, and high-speed (240 frames per second) video. These results were then compared to allow for accuracy calculations of the stopwatch and video as compared with filtered photocell timing that was used as the "gold standard." Accuracy was evaluated using maximal differences, typical error/coefficient of variation (CV), and intraclass correlation coefficients (ICCs) between the timing methods. The reliability of the video method was evaluated using the same variables in a test-retest analysis both within and between evaluators. The video timing method proved to be both highly accurate (ICC: 0.96-0.99 and CV: 0.1-0.6% as compared with the photocell method) and reliable (ICC and CV within and between evaluators: 0.99 and 0.08%, respectively). This video-based timing method provides a very rapid means of collecting a high volume of very accurate and reliable on-ice measures of skating speed and conditioning, and can easily be adapted to other testing surfaces and parameters.
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http://dx.doi.org/10.1519/JSC.0000000000000570 | DOI Listing |
Front Child Adolesc Psychiatry
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Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada.
Bipolar disorder (BD) in youth often goes unrecognized and therefore untreated. However, little is known about pathways to treatment of youth with BD and factors that influence the time taken for each stage of these pathways. In this article, we use the conceptual framework by Scott and colleagues called the Model of Pathways to Treatment as a foundation to explore the components of delay in the diagnosis and treatment of youth with BD.
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June 2025
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing, 100191, China.
An early warning model for infectious diseases is a crucial tool for timely monitoring, prevention, and control of disease outbreaks. The integration of diverse multi-source data using big data and artificial intelligence techniques has emerged as a key approach in advancing these early warning models. This paper presents a comprehensive review of widely utilized early warning models for infectious diseases around the globe.
View Article and Find Full Text PDFTransl Cancer Res
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Department of Urology, Hainan Women and Children's Medical Center, Haikou, China.
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Cureus
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Internal Medicine Department, Hamad Medical Corporation, Doha, QAT.
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View Article and Find Full Text PDFCan J Infect Dis Med Microbiol
January 2025
Department of Clinical Laboratory, Liaocheng People's Hospital, Shandong Second Medical University, Weifang, Shandong, China.
Although metagenomic next-generation sequencing (mNGS) technology has achieved notable outcomes in pathogen detection, there remains a gap in the research regarding its application in predicting the antibiotic resistance of pathogenic bacteria. This study aims to analyze the clinical application value of mNGS in predicting the resistance of carbapenem-resistant Enterobacteriaceae (CRE), as well as the relevant influencing factors, thereby providing valuable insights for clinical antimicrobial therapy. Nonduplicate isolates of bacteria collected from Liaocheng People's Hospital from April 2023 to June 2024 were selected, and CRE bacteria were screened.
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