Musculoskeletal disorders (MSDs) account for almost 70 million physician office visits per year in the United States and are the most common workplace injuries. These are conditions involving the nerves, tendons, muscles, and supporting structures of the body. Previous studies have concluded that computer users are at high risk of developing work-related musculoskeletal disorders (WRMSDs). As computer users, medical dosimetrists are at risk of developing WRMSDs, yet there is a lack of information regarding the incidence of WRMSDs among medical dosimetrists. The purpose of this study was to determine the incidence of WRMSDs and variables of workstation ergonomics that contribute to the increased risk of WRMSDs in medical dosimetrists. A Qualtrics survey was created to support the 3 research questions guiding this study. The survey was distributed to 2,646 full members of the American Association of Medical Dosimetrists (AAMD), which included only certified medical dosimetrists (CMDs), via email. The distribution of email surveys sent through the AAMD email distribution list resulted in 988 emails opened, for a contact rate of 37% (988/2646). One hundred sixty-four responses were recorded yielding a completion rate of 17% (164/988). Fifty-five percent (90/163) of participants responded that they have experienced WRMSDs. Forty-four percent (289/652) of responses indicated WRMSDs have a slight or moderate interference on work. Sixty-two percent (94/152) of participants felt that their workstations were not ergonomically designed; even greater 68% (104/153) did not feel their workstations were designed for their individually needs. Of those respondents 64% (98/152) would like to see further adaptations made to their workspaces.
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http://dx.doi.org/10.1016/j.meddos.2021.04.003 | DOI Listing |
Tech Innov Patient Support Radiat Oncol
December 2024
Baptist Institute for Health Sciences, Mbingo, Cameroon.
Although radiotherapy is critical for cancer cure and palliation, access to such expensive and sophisticated technology is very limited in low- and middle-income countries (LMIC). Cancer incidence in Africa is currently 1.5 million case per year, thus urgent and innovative solutions are required to build necessary infrastructure needed to address this global health challenge.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Radiation Oncology, Mayo Clinic in Arizona, 5881 East Mayo Boulevard, Phoenix, AZ, 85054, USA.
To propose a clinically oriented quantitative metric, Hu similarity coefficient (HSC), to evaluate contour quality, gauge the performance of auto contouring methods, and aid effective allocation of clinical resources. The HSC is defined as the ratio of the number of boundary points of the initial contour that doesn't require modifications over the number of boundary points of the final adjusted contour. To demonstrate the clinical utility of the HSC in contour evaluation, we used publicly available pelvic CT data from the Cancer Imaging Archive.
View Article and Find Full Text PDFJ Cancer Res Ther
July 2024
Department of Radiation Oncology, Allegheny Health Network Cancer Institute, Pittsburgh PA, USA.
Int J Part Ther
September 2024
South Florida Proton Therapy Institute, Delray Beach, FL 33484, USA.
Purpose: This practice parameter for the performance of proton beam radiation therapy was revised collaboratively by the American College of Radiology (ACR) and the American Radium Society (ARS). This practice parameter was developed to serve as a tool in the appropriate application of proton therapy in the care of cancer patients or other patients with conditions in which radiation therapy is indicated. It addresses clinical implementation of proton radiation therapy, including personnel qualifications, quality assurance (QA) standards, indications, and suggested documentation.
View Article and Find Full Text PDFRadiother Oncol
November 2024
Department of Radiation Oncology, University Medical Center Groningen, Groningen, the Netherlands.
Background And Purpose: This study aimed to evaluate the plan quality of our deep learning-based automated treatment planning method for robustly optimized intensity-modulated proton therapy (IMPT) plans in patients with oropharyngeal carcinoma (OPC). The assessment was conducted through a retrospective and prospective study, blindly comparing manual plans with deep learning plans.
Materials And Methods: A set of 95 OPC patients was split into training (n = 60), configuration (n = 10), test retrospective study (n = 10), and test prospective study (n = 15).
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