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| http://dx.doi.org/10.3389/fnmol.2021.645190 | DOI Listing |
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Artificial intelligence and machine learning capabilities in the detection of acute scaphoid fracture: a critical review.
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Clinical Scientific Computing, Guy's and St Thomas' NHS Foundation Trust, London, UK.
This paper discusses the current literature surrounding the potential use of artificial intelligence and machine learning models in the diagnosis of acute obvious and occult scaphoid fractures. Current studies have notable methodological flaws and are at high risk of bias, precluding meaningful comparisons with clinician performance (the current reference standard). Specific areas should be addressed in future studies to help advance the meaningful and clinical use of artificial intelligence for radiograph interpretation.
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Non-screw glenoid augmentation constructs for shoulder instability with bone loss: A biomechanical assessment of static and elastic cerclage constructs.
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August 2025
University of Alabama at Birmingham, Department of Orthopaedic Surgery, Birmingham, AL, USA.
Purpose: This study aims to compare the biomechanical performance of elastic and static suture-based cerclage systems to traditional screw constructs in the setting of modeled glenoid bony augmentation.
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Investigating the relationship between the immune response and the severity of COVID-19: a large-cohort retrospective study.
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The COVID-19 pandemic has left an indelible mark globally, presenting numerous challenges to public health. This crisis, while disruptive and impactful, has provided a unique opportunity to gather precious clinical data extensively. In this observational, case-control study, we utilized data collected at the Azienda Sanitaria Universitaria Friuli Centrale, Italy, to comprehensively characterize the immuno-inflammatory features in COVID-19 patients.
View Article and Find Full Text PDFIntroduction: Tuberculosis (TB) poses a significant threat to global health, with millions of new infections and approximately one million deaths annually. Various modeling efforts have emerged, offering tailored data-driven and physiologically-based solutions for novel and historical compounds. However, this diverse modeling panorama may lack consistency, limiting result comparability.
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