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Rationale And Objectives: Radiology teachers are well trained in their specialty; however, when working in academic institutions, faculty development and promotion through the education pathway tends to be based on their teaching knowledge and skills. The aim of this study is to assess psychometric properties of the Medicina Universidad Católica-Radiology 32 items (MEDUC-RX32), an instrument designed to evaluate the performance of postgraduate radiology teachers and to identify the best teachers.
Materials And Methods: Mixed methodology was used, including qualitative and quantitative phases. The psychometric properties of the MEDUC-RX32 survey were performed by factor analysis (validity), Cronbach alpha coefficient, and G coefficient (reliability). The residents assessed their teachers and simultaneously voted for the "best teacher," which was used as a gold standard for the receiver operating characteristic curves construction comparing their votes with the global score.
Results: A total of 28 residents answered 164 surveys. The global score was 6.23 ± 0.8 (scale from 1 to 7). The factor analysis showed six domains of the resident's perception: (1) tutorial teaching, feedback, and independent learning; (2) communication and teamwork; (3) learning objectives; (4) respectful behavior; (5) radiological report; and (6) teaching and care support. The tutor's strengths were related with respectful behavior and teamwork. The instrument is highly reliable with a Cronbach alpha of 0.937 and a G coefficient of 0.831 (with a minimum of 8 residents). The MEDUC-RX32 instrument has a sensitivity of 91.7% and specificity of 83.3% to identify tutors as best teachers with at least one vote with an area under the receiver operating characteristic curve of 0.931 with a cutoff of 5.94.
Conclusions: The MEDC-RX32 instrument is a multidimensional, valid, and highly reliable method to evaluate radiology teachers, identifying teachers with excellence in tutorial teaching in a postgraduate radiology program.
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http://dx.doi.org/10.1016/j.acra.2016.02.015 | DOI Listing |
BMC Med Imaging
December 2024
Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
Background: Diffusion-weighted imaging (DWI) can be used for quantitative tumor assessment. DWI with different models may show different aspects of tissue characteristics.
Objective: To investigate the diagnostic performance of parameters derived from monoexponential, biexponential, stretched exponential magnetic resonance diffusion weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in differentiating benign from malignant solitary pulmonary lesions (SPLs).
Comput Med Imaging Graph
December 2024
Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Liaoning 110122, China. Electronic address:
Objective: This study presents a novel framework that integrates contrastive learning and knowledge distillation to improve early ovarian cancer (OC) recurrence prediction, addressing the challenges posed by limited labeled data and tumor heterogeneity.
Methods: The research utilized CT imaging data from 585 OC patients, including 142 cases with complete follow-up information and 125 cases with unknown recurrence status. To pre-train the teacher network, 318 unlabeled images were sourced from public datasets (TCGA-OV and PLAGH-202-OC).
IET Syst Biol
December 2024
School of Computer and Information Techonology, Xinyang Normal University, Xinyang, China.
Intracranial haemorrhage (ICH) is an urgent and potentially fatal medical condition caused by brain blood vessel rupture, leading to blood accumulation in the brain tissue. Due to the pressure and damage it causes to brain tissue, ICH results in severe neurological impairment or even death. Recently, deep neural networks have been widely applied to enhance the speed and precision of ICH detection yet they are still challenged by small or subtle hemorrhages.
View Article and Find Full Text PDFInsights Imaging
November 2024
Cancer Center Amsterdam, Amsterdam, The Netherlands.
Objectives: Total tumor volume (TTV) is associated with overall and recurrence-free survival in patients with colorectal cancer liver metastases (CRLM). However, the labor-intensive nature of such manual assessments has hampered the clinical adoption of TTV as an imaging biomarker. This study aimed to develop and externally evaluate a CRLM auto-segmentation model on CT scans, to facilitate the clinical adoption of TTV.
View Article and Find Full Text PDFEur J Radiol
December 2024
Institute of Radiology, Department of Medicine, University of Udine, University Hospital S. Maria della Misericordia, p.le S. Maria della Misericordia n. 15, 33100 Udine, Italy. Electronic address:
Purpose: To assess the impact of a faculty-mentored, resident-as-teacher theoretical-practical breast MRI (B-MRI) course on the reporting completeness and accuracy of "trainee" radiology residents (RRs) with basic or no experience in conventional breast imaging (CBI) and the personal skills and critical thinking of experienced, "teacher" RRs.
Methods: Six teacher-RRs from the Udine University residency program (URP) preliminarily selected and reported 55 B-MRI cases under faculty supervision. Twelve trainee-RRs (six from Udine URP and six from Vienna URP, with basic and no experience in CBI, respectively) underwent seven days of self-study on selected material, followed by a pre-training reporting test (pre-TRT) on 15 of the 55 B-MRI cases.
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