Background: The foundations of an effective and evidence-based training program are the metrics, which characterize optimal performance.
Purposes: To develop, operationally define, and seek consensus from procedure experts on the metrics that best characterize a reference approach to the performance of a closed reduction and internal fixation of a 31A2 unstable pertrochanteric fracture with a cephalomedullary nail with distal locking through the proximal guide.
Methods: A Metrics Group consisting of 3 senior orthopaedic surgeons, a surgeon/medical scientist, an education expert and a behavioural scientist deconstructed the performance of the selected fixation procedure and defined performance metrics. At a modified Delphi meeting, 32 senior orthopaedic and trauma surgeons from 18 countries critiqued these metrics and their operational definitions before reaching consensus.
Results: Initially performance metrics consisting of 14 Phases with 62 Steps, 84 errors and 20 Sentinel errors were identified that characterize the safe and effective performance of the procedure. During the Delphi panel meeting these were modified and consensus was reached on 15 Phases (1 added, p = 0.967)) with 75 Steps (14 added and 1 deleted; p = 0.028), 88 errors (10 added and 6 deleted; p = 0.47), and 28 Sentinel errors (8 added; p = 0.107). Pre and Post Delphi characterizations were highly correlated (r = 0.81-0.94).
Conclusions: Surgical procedures can be broken down into constituent, essential, and elemental tasks necessary for the safe and effective completion of a reference approach to a specified procedure. Procedure experts from 18 countries reached consensus on performance metrics for the fixation procedure. This metric-based characterization should form the basis of more quantitative validation studies to guide the construction of a proficiency-based progression training curriculum.
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http://dx.doi.org/10.1016/j.injury.2018.09.019 | DOI Listing |
Commun Biol
January 2025
Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
Single cell studies have transformed our understanding of cellular heterogeneity in disease but the need for fresh starting material can be an obstacle, especially in the context of international multicenter studies and archived tissue. We developed a protocol to obtain high-quality cells and nuclei from dissected human skeletal muscle archived in the preservative Allprotect® Tissue Reagent. After fluorescent imaging microscopy confirmed intact nuclei, we performed four protocol variations that compared sequencing metrics between cells and nuclei enriched by either filtering or flow cytometry sorting.
View Article and Find Full Text PDFSci Rep
January 2025
University of Ghana, P.O. Box 134, Legon-Accra, Ghana.
Sentiment analysis has become a difficult and important task in the current world. Because of several features of data, including abbreviations, length of tweet, and spelling error, there should be some other non-conventional methods to achieve the accurate results and overcome the current issue. In other words, because of those issues, conventional approaches cannot perform well and accomplish results with high efficiency.
View Article and Find Full Text PDFSci Rep
January 2025
Electronics and Communication Engineering Dept. Faculty of Engineering, Horus University, New Damietta, Egypt.
Electric vehicles (EVs) rely heavily on lithium-ion battery packs as essential energy storage components. However, inconsistencies in cell characteristics and operating conditions can lead to imbalanced state of charge (SOC) levels, resulting in reduced capacity and accelerated degradation. This study presents an active cell balancing method optimized for both charging and discharging scenarios, aiming to equalize SOC across cells and improve overall pack performance.
View Article and Find Full Text PDFNat Commun
January 2025
Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
Spatial protein expression technologies can map cellular content and organization by simultaneously quantifying the expression of >40 proteins at subcellular resolution within intact tissue sections and cell lines. However, necessary image segmentation to single cells is challenging and error prone, easily confounding the interpretation of cellular phenotypes and cell clusters. To address these limitations, we present STARLING, a probabilistic machine learning model designed to quantify cell populations from spatial protein expression data while accounting for segmentation errors.
View Article and Find Full Text PDFBMC Musculoskelet Disord
January 2025
Division of Orthopaedic Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada.
Background: To summarize the statistical performance of machine learning in predicting revision, secondary knee injury, or reoperations following anterior cruciate ligament reconstruction (ACLR), and to provide a general overview of the statistical performance of these models.
Methods: Three online databases (PubMed, MEDLINE, EMBASE) were searched from database inception to February 6, 2024, to identify literature on the use of machine learning to predict revision, secondary knee injury (e.g.
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