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Rationale and Objectives In current practice, radiologists interpret digital images, including a substantial amount of volumetric images. We hypothesized that interpretation of a stack of a volumetric data set demands different skills than interpretation of two-dimensional (2D) cross-sectional images. This study aimed to investigate and compare knowledge and skills used for interpretation of volumetric versus 2D images. Materials and Methods Twenty radiology clerks were asked to think out loud while reading four or five volumetric computed tomography (CT) images in stack mode and four or five 2D CT images. Cases were presented in a digital testing program allowing stack viewing of volumetric data sets and changing views and window settings. Thoughts verbalized by the participants were registered and coded by a framework of knowledge and skills concerning three components: perception, analysis, and synthesis. The components were subdivided into 16 discrete knowledge and skill elements. A within-subject analysis was performed to compare cognitive processes during volumetric image readings versus 2D cross-sectional image readings. Results Most utterances contained knowledge and skills concerning perception (46%). A smaller part involved synthesis (31%) and analysis (23%). More utterances regarded perception in volumetric image interpretation than in 2D image interpretation (Median 48% vs 35%; z = −3.9; P < .001). Synthesis was less prominent in volumetric than in 2D image interpretation (Median 28% vs 42%; z = −3.9; P < .001). No differences were found in analysis utterances. Conclusions Cognitive processes in volumetric and 2D cross-sectional image interpretation differ substantially. Volumetric image interpretation draws predominantly on perceptual processes, whereas 2D image interpretation is mainly characterized by synthesis. The results encourage the use of volumetric images for teaching and testing perceptual skills. [ABSTRACT FROM AUTHOR]

Rationale and Objectives Radiology practice has become increasingly based on volumetric images (VIs), but tests in medical education still mainly involve two-dimensional (2D) images. We created a novel, digital, VI test and hypothesized that scores on this test would better reflect radiological anatomy skills than scores on a traditional 2D image test. To evaluate external validity we correlated VI and 2D image test scores with anatomy cadaver–based test scores. Materials and Methods In 2012, 246 medical students completed one of two comparable versions (A and B) of a digital radiology test, each containing 20 2D image and 20 VI questions. Thirty-three of these participants also took a human cadaver anatomy test. Mean scores and reliabilities of the 2D image and VI subtests were compared and correlated with human cadaver anatomy test scores. Participants received a questionnaire about perceived representativeness and difficulty of the radiology test. Results Human cadaver test scores were not correlated with 2D image scores, but significantly correlated with VI scores ( r = 0.44, P < .05). Cronbach's α reliability was 0.49 (A) and 0.65 (B) for the 2D image subtests and 0.65 (A) and 0.71 (B) for VI subtests. Mean VI scores (74.4%, standard deviation 2.9) were significantly lower than 2D image scores (83.8%, standard deviation 2.4) in version A ( P < .001). VI questions were considered more representative of clinical practice and education than 2D image questions and less difficult (both P < .001). Conclusions VI tests show higher reliability, a significant correlation with human cadaver test scores, and are considered more representative for clinical practice than tests with 2D images. [ABSTRACT FROM AUTHOR]

What makes an expert university teacher? Answers to this question can be found in a multitude of publications, but so far there has been little insight into what these answers have in common. More common ground regarding what teacher expertise entails is necessary for research and support of the professional development of university teachers. To this end, this study aims to find consensus regarding what constitutes teacher expertise in higher education by identifying teacher tasks. We conducted a systematic review in which 46 frameworks for teacher expertise from research and practice contexts were identified, analysed, and synthesised. Six teacher tasks were distinguished: 'teaching and supporting learning', 'educational design', 'assessment and feedback', 'educational leadership and management', 'educational scholarship and research', and 'professional development'. Additionally, the following three dimensions for task-related development were found: 'better task performance', 'ability to carry out a greater variety of tasks', and 'a larger sphere of influence'. We present and visualise these tasks and task-related dimensions for development as the UNIversity Teacher Expertise (UNITE) synthesis. The synthesis both reflects and contributes to consensus about teacher expertise in higher education, which further research can build on. In academic practice, this synthesis could support teachers' reflection on their professional development and inform faculty development programmes and career policies. Further research is required to validate the results of this study, in particular the dimensions for development. Other suggested areas for future research are to explore how development in different teacher tasks is interrelated, as well as developing and investigating tools and interventions based on the perspective and findings of this study. • Consensus is lacking on what constitutes teacher expertise in higher education. • Teacher expertise frameworks were reviewed and synthesised to identify consensus. • Six teacher tasks were found, including leadership and scholarship. • Three developmental dimensions were found, including task variety and influence. • Higher education policies should incorporate the various tasks and dimensions. [ABSTRACT FROM AUTHOR]