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An Automated Clinical Laboratory Decision Support System for Test Utilization, Medical Necessity Verification and Payment Processing.
Interact J Med Res
January 2025
University of California, San Francisco, Department of Laboratory Medicine, San Francisco, US.
Physicians could improve the efficiency of the healthcare system if a reliable resource were available to aid them in better understanding, selecting, and interpreting the diagnostic laboratory tests. It has been well established and widely recognized that (a) laboratory testing provides 70-85% of the objective data that physicians use in diagnosis and treatment of their patients, (b) orders for laboratory tests in the U.S.
View Article and Find Full Text PDFElevated Hepatic Copper Content in Porto-Sinusoidal Vascular Disorder (PSVD): Leading Down a Wrong Track.
Liver Int
February 2025
Division of Gastroenterology and Hepatology, Department of Medicine III, Medical University of Vienna, Vienna, Austria.
Background And Aims: Porto-sinusoidal vascular disorder (PSVD) is a rare vascular liver disorder characterised by specific histological findings in the absence of cirrhosis, which is poorly understood in terms of pathophysiology. While elevated hepatic copper content serves as diagnostic hallmark in Wilson disease (WD), hepatic copper content has not yet been investigated in PSVD.
Methods: Patients with a verified diagnosis of PSVD at the Medical University of Vienna and available hepatic copper content at the time of diagnosis of PSVD were retrospectively included.
Ethics: The Elixir of Publications.
Indian J Radiol Imaging
January 2025
Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
Scientific papers are the driving force for research, information dissemination, and policymaking that directly impacts society. Thus, ethical practices are the elixir of publications. Adherence to ethical practices promotes integrity in research and publication.
View Article and Find Full Text PDFA Multianalyte Machine Learning Model to Detect Wrong Blood in Complete Blood Count Tube Errors in a Pediatric Setting.
Clin Chem
January 2025
Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States.
Background: Multianalyte machine learning (ML) models can potentially identify previously undetectable wrong blood in tube (WBIT) errors, improving upon current single-analyte delta check methodology. However, WBIT detection model performance has not been assessed in a real-world, low-prevalence context. To estimate real-world positive predictive values, we propose a methodology to assess WBIT detection models by evaluating the impact of missing data and by using a "low prevalence" validation data set.
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