Repeated testing improves achievement in a blended learning approach for risk competence training of medical students: results of a randomized controlled trial

Background: Adequate estimation and communication of risks is a critical competence of physicians. Due to an evident lack of these competences, effective training addressing risk competence during medical education is needed. Test-enhanced learning has been shown to produce marked effects on achievements. This study aimed to investigate the effect of repeated tests implemented on top of a blended learning program for risk competence. Methods: We introduced a blended-learning curriculum for risk estimation and risk communication based on a set of operationalized learning objectives, which was integrated into a mandatory course “Evidence-based Medicine” for third-year students. A randomized controlled trial addressed the effect of repeated testing on achievement as measured by the students’ pre- and post-training score (nine multiple-choice items). Basic numeracy and statistical literacy were assessed at baseline. Analysis relied on descriptive statistics (histograms, box plots, scatter plots, and summary of descriptive measures), bootstrapped confidence intervals, analysis of covariance (ANCOVA), and effect sizes (Cohen’s d, r) based on adjusted means and standard deviations. Results: All of the 114 students enrolled in the course consented to take part in the study and were assigned to either the intervention or control group (both: n = 57) by balanced randomization. Five participants dropped out due to non-compliance (control: 4, intervention: 1). Both groups profited considerably from the program in general (Cohen’s d for overall pre vs. post scores: 2.61). Repeated testing yielded an additional positive effect: while the covariate (baseline score) exhibits no relation to the post-intervention score, F(1, 106) = 2.88, p > .05, there was a significant effect of the intervention (repeated tests scenario) on learning achievement, F(1106) = 12.72, p < .05, d = .94, r = .42 (95% CI: [.26, .57]). However, in the subgroup of participants with a high initial numeracy score no similar effect could be observed. Conclusion: Dedicated training can improve relevant components of risk competence of medical students. An already promising overall effect of the blended learning approach can be improved significantly by implementing a test-enhanced learning design, namely repeated testing. As students with a high initial numeracy score did not profit equally from repeated testing, target-group specific opt-out may be offered

Bioinspired Breathable Architecture for Water Harvesting

Thuja plicata is a coniferous tree which displays remarkable water channelling properties. In this article, an easily fabricated mesh inspired by the hierarchical macro surface structure of Thuja plicata branchlets is described which emulates this efficient water collection behaviour. The key parameters are shown to be the pore size, pore angle, mesh rotation, tilt angle (branch droop) and layering (branch overlap). Envisaged societal applications include water harvesting and low cost breathable architecture for developing countries

Prototypes for Content-Based Image Retrieval in Clinical Practice

Content-based image retrieval (CBIR) has been proposed as key technology for computer-aided diagnostics (CAD). This paper reviews the state of the art and future challenges in CBIR for CAD applied to clinical practice

Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. In this study, we developed DeepGlioma, a rapid ($< 90$ seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas. DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH); a rapid, label-free, non-consumptive, optical imaging method; and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of patients with diffuse glioma ($n=153$) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular classification accuracy of $93.3\pm 1.6\%$. Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular screening of patients with diffuse glioma.Comment: Paper published in Nature Medicin

methods and results of a systematical exploration

E-learning in medicine traditionally concentrates on case oriented or problem oriented learning scenarios, the development of multimedia courseware or the implementation of simulators. This paper aims at a systematic exploration of actual and new challenges for E-learning in the medical domain. The exploration is based on the analysis of the scientific discourse in the field of Medical Education. The analysis starts from text based sources: the concept hierarchy of the Medical Subject Headings, the profiles of the relevant scientific associations, and the scientific program of scientific conferences or annual meetings. These sources are subjected to conceptual analysis, supported by network visualization tools and supplemented by network theoretic indices (Betweeness Centrality). As a result, the main concerns of the Medical Education community and their modifications during the last six years can be identified. The analysis discovers new challenges, which result from central issues of Medical Education, namely from e.g. curricular and faculty development or the sustainable integration of postgraduate education and continuing medial education. The main challenges are: 1) the implementation of integrative conceptions of the application of learning management systems (LMS) and 2) the necessity of combining aspects of organizational development, knowledge management and learning management within the scope of a comprehensive learning life cycle management.E-Learning-Ansätze in der Medizin konzentrieren sich traditionell besonders auf die Unterstützung fall- oder problemorientierten Lernens, der Erstellung multimedialer Lehrmaterialien oder der Implementierung von Simulatoren. Die Studie zielt darauf, systematisch aktuelle und neue Herausforderungen für das E-Learning in der Medizin durch eine Analyse des fachwissenschaftlichen Diskurses im Bereich "Medical Education" zu gewinnen. Als Quellenmaterial werden die Medical Subject Headings, die Profile der relevanten Fachgesellschaften des Bereichs sowie Programme wissenschaftlicher Konferenzen genutzt. Eine konzeptuelle Analyse, unterstützt durch eine Netzvisualisierung und die Berechnung netzwerktheoretischer Kennzahlen (Betweeness Centrality) in Konzeptnetzen zielt auf die Identifizierung thematischer Schwerpunkte, ihrer Beziehungen und ihrer zeitlichen Verlagerungen während der letzten sechs Jahre. Dabei zeigt sich, dass zentrale Anliegen des Bereichs "Medical Education", wie Curricularentwicklung, Faculty Development oder die Integration der postgraduierten Ausbildung bzw. ärztlichen Weiterbildung, neue Herausforderungen an E-Learning-Ansätze stellen. Hierzu zählen vor allem 1) die Entwicklung integrativer Konzepte für den Einsatz von Lernmanagementsystemen und 2) die Notwendigkeit, E-Learning-Projekte stärker als bisher im Schnittbereich von Organisationsentwicklung, Wissensmanagement und Lernmanagement zu konzipieren