Academic radiologists spend considerable amounts of time and effort providing nonclinical value-added services in the realms of teaching, research, and administration that are not reimbursable through traditional relative value units (RVUs) under the resource-based relative value scale. Numerous systems of academic RVUs have been proposed by medicine, surgery, and radiology programs to measure and reward these nonclinical contributions. In this article the authors (1) describe the traditional clinical RVU model of reimbursement; (2) review attempts to develop academic compensation models targeted toward research, teaching, and administration; and (3) describe possible models for academic productivity compensation.
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http://dx.doi.org/10.1016/j.jacr.2019.05.021 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada.
Limiting climate change to targets enshrined in the Paris Agreement will require both deep decarbonization of the energy system and the deployment of carbon dioxide removal at potentially large scale (gigatons of annual removal). Nations are pursuing direct air capture to compensate for inertia in the expansion of low-carbon energy systems, decarbonize hard-to-abate sectors, and address legacy emissions. Global assessments of this technology have failed to integrate factors that affect net capture and removal cost, including ambient conditions like temperature and humidity, as well as emission factors of electricity and natural gas systems.
View Article and Find Full Text PDFOtol Neurotol
February 2025
Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins Hospital, Baltimore, MD.
Objective: The physician-scientist workforce is shrinking in the United States. Academic otologists/neurotologists face a diverse set of barriers to successful careers. We aimed to characterize the factors affecting contemporary otology/neurotology surgeon-scientists.
View Article and Find Full Text PDFJ Magn Reson Imaging
January 2025
Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway.
Background: Deep learning-based segmentation of brain metastases relies on large amounts of fully annotated data by domain experts. Semi-supervised learning offers potential efficient methods to improve model performance without excessive annotation burden.
Purpose: This work tests the viability of semi-supervision for brain metastases segmentation.
STAR Protoc
January 2025
Department of Medicine, Division of Hematology, Oncology, and Transplantation, University of Minnesota Medical School, Minneapolis, MN, USA; Masonic Cancer Center, University of Minnesota Medical School, Minneapolis, MN, USA. Electronic address:
Tumor Treating Fields (TTFields) are electric fields clinically approved for cancer treatment, delivered via arrays attached to the patient's skin. Here, we present a protocol for applying TTFields to torso orthotopic and subcutaneous mouse tumor models using the inovivo system. We guide users on proper system component connections, study protocol design, mouse fur depilation, array application, and treatment condition adjustment and monitoring.
View Article and Find Full Text PDFJ Alzheimers Dis
January 2025
Department of Neurology and the Franke Barrow Global Neuroscience Education Center, Barrow Neurological Institute, Phoenix, AZ, USA.
Background: The aim of this study was to examine the potential added value of including neuropsychiatric symptoms (NPS) in machine learning (ML) models, along with demographic features and Alzheimer's disease (AD) biomarkers, to predict decline or non-decline in global and domain-specific cognitive scores among community-dwelling older adults.
Objective: To evaluate the impact of adding NPS to AD biomarkers on ML model accuracy in predicting cognitive decline among older adults.
Methods: The study was conducted in the setting of the Mayo Clinic Study of Aging, including participants aged ≥ 50 years with information on demographics (i.
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