Introduction: Diagnostic radiographers working in oncology will have frequent contact with the same patients over a prolonged period. This can be mentally exhausting for the radiographer. Compassion fatigue (CF) occurs after repeated exposure to stressful situations and it can become overwhelming, leading to irritability and decreased empathy. CF has been known to affect many healthcare professions, however few studies have examined diagnostic radiographers, nor if the current support systems are suitable.
Methods: An exploratory study was conducted as part of a local quality improvement project. An anonymised questionnaire was sent to all radiographers in a single oncology hospital within the UK to assess if the support provided met their needs.
Results: Sixty percent of those questioned responded. Almost half found their work affected their mental wellbeing, but they felt they could manage this stress at work. Almost all felt that some sort of support should be offered to the radiographers. The most popular options were already provided by the hospital, however many felt they were not accessible for a variety of reasons. When discussed further, it was found that the timings were prohibitive as most were held when they could not attend.
Conclusion: Diagnostic radiographers working in oncology settings are at risk of CF. Although support structures are in place, they may not currently meet the needs of this staff group and at times are inaccessible.
Implications For Practice: Providing specific, accessible support for diagnostic radiographers will help reduce the potential effects of CF, reduce stress-related sickness and ultimately improve the service for patients.
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http://dx.doi.org/10.1016/j.jmir.2020.11.008 | DOI Listing |
JAMA Neurol
January 2025
Department of Neurology, Xuanwu Hospital Capital Medical University, National Center for Neurological Disorders, Beijing, China.
Importance: Autoantibodies targeting astrocytes, such as those against glial fibrillary acidic protein (GFAP) or aquaporin protein 4, are crucial diagnostic markers for autoimmune astrocytopathy among central nervous system (CNS) autoimmune disorders. However, diagnosis remains challenging for patients lacking specific autoantibodies.
Objective: To characterize a syndrome of unknown meningoencephalomyelitis associated with an astrocytic autoantibody.
Infection
January 2025
Department of Clinical Infectious Diseases, Research Center Borstel, Leibniz Lung Center, Parkallee 35, Borstel, Germany.
Purpose: Deciding whether to provide preventive treatment to contacts of individuals with multidrug-resistant (MDR) tuberculosis is complex.
Methods: We present the diagnostic pathways, clinical course and outcome of tuberculosis treatment in eight siblings from a single family. Tuberculosis disease was diagnosed by Mycobacterium tuberculosis culture and molecular detection of M.
Oral Radiol
January 2025
Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Av. Limeira, 901, Areião, Piracicaba, SP, 13414-903, Brazil.
Objectives: To assess the influence of a handheld X-ray unit in the diagnosis of proximal caries lesions using different digital systems by comparing with a wall-mounted unit.
Methods: Radiographs of 40 human teeth were acquired using the Eagle X-ray handheld unit (Alliage, São Paulo, Brazil) set at 2.5 mA, 60 kVp and an exposure time of 0.
Clin Oral Investig
January 2025
Department of Restorative Dentistry, School of Dentistry of Ribeirão Preto, University of São Paulo (USP), Ribeirão Preto, SP, Brazil.
Objectives: To evaluate cases of persistent apical periodontitis (PAP) and what are the imaging and clinical aspects that could be considered in the PAP diagnosis and in their treatment decision-making process.
Methodology: 423 patients with apical periodontitis at the time of non-surgical root canal treatment (NSRCT) were followed-up for at least 1 year. Periapical radiographic images were used to compare and determine periapical status at each time using the PAI scoring system.
Radiology
January 2025
Stanford University School of Medicine, Department of Radiation Oncology, Stanford, CA, US.
Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician variability. Purpose To develop and evaluate an ensemble deep learning model for automating identification and segmentation of lung tumors on CT scans. Materials and Methods A retrospective study was conducted between July 2019 and November 2024 using a large dataset of CT simulation scans and clinical lung tumor segmentations from radiotherapy plans.
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