The Centers for Medicare and Medicaid Services (CMS) have prioritized the objective of optimizing quality healthcare though quality improvement initiatives, yet research on dermatology-specific QI programs and their perceptions among dermatology residents remains limited. We explore residents' opinions of a dermatology-specific QI scholarly project curriculum implemented at University of Colorado Denver (UCD) in 2010 and also evaluate residents' attitudes regarding the value of this curriculum in aiding them to meet ACGME core competencies.
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Rhythmic Exercises Before Basketball Training: A Study on Motor Skills, Static Balance, and Reaction Speed in School-Aged Children.
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December 2024
Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Rome Open University, Rome, Italy; and.
Racil, G, Padulo, J, Trabelsi, Y, Frizziero, A, Russo, L, and Migliaccio, GM. Rhythmic exercises before basketball training: A study on motor skills, static balance, and reaction speed in school-aged children. J Strength Cond Res 38(12): e761-e768, 2024-The aim of this study was to investigate the effects of combining rhythmic exercises with basketball training on the improvement of basic motor and physical skills in children.
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Author Affiliations: Castner Incorporated, Grand Island, NY (Dr Castner); Health Policy, Management, and Behavior, School of Public Health, University at Albany, Albany, New York (Dr Castner); Stony Brook University School of Nursing, Stony Brook, NY (Ms Zazzera); and Nursing Research and Evidence-Based Practice, Penn Medicine Lancaster General Health, Lancaster, PA (Dr Burchill).
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Department of Orthopaedics and Rehabilitation, Yale School of Medicine, New Haven, CT.
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Association between cardiometabolic index and overactive bladder in adult American women: A cross-sectional study.
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Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, China.
Accurate detection of fabric defects is crucial for quality control in the textile industry. However, the task of fabric defect detection remains highly challenging due to the complex textures and diverse defect patterns. To address the issues of inaccurate localization and false positives caused by complex textures and varying defect sizes, this paper proposes an improved YOLOv8-based fabric defect detection method.
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