99 research outputs found

    Real-time imaging required for optimal echocardiographic assessment of aortic valve calcification

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    Introduction Aortic valve calcification (AVC), even without haemodynamic significance, may be prognostically import as an expression of generalized atherosclerosis, but techniques for echocardiographic assessment are essentially unexplored. Methods Two-dimensional (2D) echocardiographic recordings (Philips IE33) of the aortic valve in short-axis and long-axis views were performed in 185 consecutive patients within 1 week before surgery for aortic stenosis (n = 109, AS), aortic regurgitation (n = 61, AR), their combination (n = 8) or dilation of the ascending aorta (n = 7). The grey scale mean (GSMn) of the aortic valve in an end-diastolic short-axis still frame was measured. The same frame was scored visually 15 as indicating that the aortic valve was normal, thick, or had mild, moderate or severe calcification. The visual echodensity of each leaflet was determined real time applying the same 5-grade scoring system for each leaflet, and the average for the whole valve was calculated. Finally, a similar calcification score for the whole valve based on inspection and palpation by the surgeon was noted. Results Visual assessment of real-time images using the proposed scoring system showed better correlation with the surgical evaluation of the degree of valve calcification (r = 0.83, P<0.001) compared to evaluation of stop frames by visual assessment (r = 0.66, P<0.001) or the GSMn score (r = 0.64, P<0.001). High inter- and intra-observer correlations were observed for real-time visual score (both intraclass correlation coefficient = 0.93). Conclusion Real-time evaluation of the level of AVC is superior to using stop frames assessed either visually or by dedicated computer grey scale measurement software

    Does individual learning styles influence the choice to use a web-based ECG learning programme in a blended learning setting?

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    <p>Abstract</p> <p>Background</p> <p>The compressed curriculum in modern knowledge-intensive medicine demands useful tools to achieve approved learning aims in a limited space of time. Web-based learning can be used in different ways to enhance learning. Little is however known regarding its optimal utilisation. Our aim was to investigate if the individual learning styles of medical students influence the choice to use a web-based ECG learning programme in a blended learning setting.</p> <p>Methods</p> <p>The programme, with three types of modules (learning content, self-assessment questions and interactive ECG interpretation training), was offered on a voluntary basis during a face to face ECG learning course for undergraduate medical students. The Index of Learning Styles (ILS) and a general questionnaire including questions about computer and Internet usage, preferred future speciality and prior experience of E-learning were used to explore different factors related to the choice of using the programme or not.</p> <p>Results</p> <p>93 (76%) out of 123 students answered the ILS instrument and 91 the general questionnaire. 55 students (59%) were defined as users of the web-based ECG-interpretation programme. Cronbach's alpha was analysed with coefficients above 0.7 in all of the four dimensions of ILS. There were no significant differences with regard to learning styles, as assessed by ILS, between the user and non-user groups; Active/Reflective; Visual/Verbal; Sensing/Intuitive; and Sequential/Global (p = 0.56-0.96). Neither did gender, prior experience of E-learning or preference for future speciality differ between groups.</p> <p>Conclusion</p> <p>Among medical students, neither learning styles according to ILS, nor a number of other characteristics seem to influence the choice to use a web-based ECG programme. This finding was consistent also when the usage of the different modules in the programme were considered. Thus, the findings suggest that web-based learning may attract a broad variety of medical students.</p

    GH and the cardiovascular system: an update on a topic at heart

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    Human plasma protein N-glycosylation

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