Predictors of stigma in a sample of mental health professionals: Network and moderator analysis on gender, years of experience, personality traits, and levels of burnout.
CONCLUSIONS.: Some personality traits may be accompanied by better empathic and communication skills, and may have a protective role against stigma. Moreover, burnout can increase stigma, in particular in subjects with specific personality traits. Assessing personality and burnout levels could help in identifying mental health professionals at higher risk of developing stigma. Future studies should determine whether targeted interventions in mental health professionals at risk of developing stigma may be effective in stigma prevention. PMID: 32093794 [PubMed - in process]
Publication date: Available online 13 July 2020Source: Journal of Vascular and Interventional RadiologyAuthor(s): Eric M. Chang, Narek Shaverdian, Nina Capiro, Michael L. Steinberg, Ann C. Raldow
Publication date: Available online 13 July 2020Source: Journal of Vascular and Interventional RadiologyAuthor(s): James J. Morrison, Albert Jiao, Sean Robinson, Younes Jahangiri, John A. Kaufman
Publication date: Available online 13 July 2020Source: Journal of the American College of RadiologyAuthor(s): Lori A. Deitte, Asim Z. Mian, Shadi A. Esfahani, Jiun-Yiing Hu
Publication date: Available online 13 July 2020Source: Journal of the American College of RadiologyAuthor(s): Mourão Rodrigo, Correa Diogo, Ventura Nina, Pereira Ronaldo
ConclusionsThe study suggests that EA-PM TCC cannot be diagnosed based on the classical indirect radiological signs of TCC, but can be identified by prominence of the posterior subtalar joint.
ConclusionFDG-PET/CT may steer the diagnosis (particularly thanks to a relatively high PPV and value of semiquantitative measurements), but cannot always classify vertebral bone lesions as malignant or benign with sufficient certainty. In these cases, biopsy and/or follow-up remain necessary to establish a final diagnosis.
ConclusionWe achieved promising results with this computer-aided diagnosis method that we tried to develop using convolutional neural networks based on transfer learning. This method can help clinicians for the diagnosis of hip osteoarthritis while interpreting plain pelvic radiographs, also provides assistance for a second objective interpretation. It may also reduce the need for advanced imaging methods in the diagnosis of hip osteoarthritis.
ConclusionThe shorthand bone age method and the automated algorithm produced values that are in agreement with the gold standard while reducing analysis time.