Learning pathways composed of core subjects with features of reducing cognitive load have better learning outcomes.

BACKGROUND: Changing the course duration or timing of subjects in learning pathways would influence medical students' learning outcomes. Curriculum designers need to consider the strategy of reducing cognitive load and evaluate it continuously. Our institution underwent gradual curricular changes characterized by reducing cognitive load since 2000. Therefore, we wanted to explore the impact of this strategy on our previous cohorts. METHODS: This cohort study explored learning pathways across academic years of more than a decade since 2000. Eight hundred eighty-two medical students between 2006 and 2012 were included eventually. Learning outcomes included an average and individual scores of subjects in different stages. Core subjects were identified as those where changes in duration or timing would influence learning outcomes and constitute different learning pathways. We examined whether the promising learning pathway defined as the pathway with the most features of reducing cognitive load has higher learning outcomes than other learning pathways in the exploring dataset. The relationship between features and learning outcomes was validated by learning pathways selected in the remaining dataset. RESULTS: We found nine core subjects, constituting four different learning pathways. Two features of extended course duration and increased proximity between core subjects of basic science and clinical medicine were identified in the promising learning pathway 2012, which also had the highest learning outcomes. Other pathways had some of the features, and pathway 2006 without such features had the lowest learning outcomes. The relationship between higher learning outcomes and cognitive load-reducing features was validated by comparing learning outcomes in two pathways with and without similar features of the promising learning pathway. CONCLUSION: An approach to finding a promising learning pathway facilitating students' learning outcomes was validated. Curricular designers may implement similar design to explore the promising learning pathway while considering potential confounding factors, including students, medical educators, and learning design of the course.

Using machine learning to identify key subject categories predicting the pre-clerkship and clerkship performance: 8-year cohort study.

BACKGROUND: Medical students need to build a solid foundation of knowledge to become physicians. Clerkship is often considered the first transition point, and clerkship performance is essential for their development. We hope to identify subjects that could predict the clerkship performance, thus helping medical students learn more efficiently to achieve high clerkship performance. METHODS: This cohort study collected background and academic data from medical students who graduated between 2011 and 2019. Prediction models were developed by machine learning techniques to identify the affecting features in predicting the pre-clerkship performance and clerkship performance. Following serial processes of data collection, data preprocessing before machine learning, and techniques and performance of machine learning, different machine learning models were trained and validated using the 10-fold cross-validation method. RESULTS: Thirteen subjects from the pre-med stage and 10 subjects from the basic medical science stage with an area under the ROC curve (AUC) >0.7 for either pre-clerkship performance or clerkship performance were found. In each subject category, medical humanities and sociology in social science, chemistry, and physician scientist-related training in basic science, and pharmacology, immunology-microbiology, and histology in basic medical science have predictive abilities for clerkship performance above the top tertile. Using a machine learning technique based on random forest, the prediction model predicted clerkship performance with 95% accuracy and 88% AUC. CONCLUSION: Clerkship performance was predicted by selected subjects or combination of different subject categories in the pre-med and basic medical science stages. The demonstrated predictive ability of subjects or categories in the medical program may facilitate students' understanding of how these subjects or categories of the medical program relate to their performance in the clerkship to enhance their preparedness for the clerkship.

Medical students' self-assessed efficacy and satisfaction with training on endotracheal intubation and central venous catheterization with smart glasses in Taiwan: a non-equivalent control-group pre- and post-test study

PURPOSE: Endotracheal intubation and central venous catheterization are essential procedures in clinical practice. Simulation-based technology such as smart glasses has been used to facilitate medical students' training on these procedures. We investigated medical students' self-assessed efficacy and satisfaction regarding the practice and training of these procedures with smart glasses in Taiwan. METHODS: This observational study enrolled 145 medical students in the 5th and 6th years participating in clerkships at Taipei Veterans General Hospital between October 2020 and December 2021. Students were divided into the smart glasses or the control group and received training at a workshop. The primary outcomes included students' pre- and post-intervention scores for self-assessed efficacy and satisfaction with the training tool, instructor's teaching, and the workshop. RESULTS: The pre-intervention scores for self-assessed efficacy of 5th- and 6th-year medical students in endotracheal intubation and central venous catheterization procedures showed no significant difference. The post-intervention score of self-assessed efficacy in the smart glasses group was better than that of the control group. Moreover, 6th-year medical students in the smart glasses group showed higher satisfaction with the training tool, instructor's teaching, and workshop than those in the control group. CONCLUSION: Smart glasses served as a suitable simulation tool for endotracheal intubation and central venous catheterization procedures training in medical students. Medical students practicing with smart glasses showed improved self-assessed efficacy and higher satisfaction with training, especially for procedural steps in a space-limited field. Simulation training on procedural skills with smart glasses in 5th-year medical students may be adjusted to improve their satisfaction.

Reactive oxygen species scavenging properties of ZrO2-CeO2 solid solution nanoparticles.

AIMS: The hypothesis that an increase in defects in cerium oxide (CeO(2)) nanoparticles induced by solid solutions with differences in valency and ionic radius of the solute will yield superior reactive oxygen species (ROS) scavengers at room temperature will be tested. METHODS: Solid solutions of zirconium in CeO(2), that is, Ce(x)Zr(1-x)O(2) nanoparticles, were synthesized by a reverse micelle method. Their crystal structures, particle sizes and level of agglomeration were characterized. The nanoparticles' activities to scavenge ROS were tested in response to hydrogen peroxide at physiological levels and room temperature using an enzyme peroxidase-based assay. RESULTS: Solid solutions of Zr in CeO(2) nanoparticles enhanced ROS scavenging fourfold. The hypothesis is confirmed that more defects are formed and that the scavenging activities of Ce(x)Zr(1-x)O(2) correlate to the nanoparticles' oxygen-storage capacity. CONCLUSIONS: The antioxidant efficacy of CeO(2) nanoparticles can be enhanced by dissolving zirconium in the CeO(2) lattice. The Ce(x)Zr(1-x)O(2) nanoparticles act as an enhanced catalyst at room temperature that scavenges ROS. Increased efficacy will enable lower nanoparticle dosages to protect cells from ROS, thus increasing the therapeutic width of these compounds.