“…Second, the tests did not directly evaluate the manual skills of participants, but only the final product and workflow measured in the time needed to complete the task. 11 42 According to the IMSS consensus, the end-product evaluation tool is sufficiently accurate for basic level courses. 6 Moreover, this study evaluates an intense but short training period (4 days), which is insufficient to achieve microsurgical competency and determine a precise learning curve but provides an introduction of basic microsurgical skills.…”
Background Repetitive training is essential for microsurgical performance. This study aimed to compare the improvement in basic microsurgical skills using two learning methods: stationary microsurgical course with tutor supervision and self-learning based on digital instructional materials. We hypothesized that video-based training provides noninferior improvement in basic microsurgical skills.
Methods In this prospective study, 80 participants with no prior microsurgical experience were randomly divided into two groups: the control group, trained under the supervision of a microsurgical tutor, and the intervention group, where knowledge was based on commonly available online instructional videos without tutor supervision. Three blinded expert microsurgeons evaluated the improvement in basic microsurgical skills in both groups. The evaluation included an end-to-end anastomosis test using the Ten-Point Microsurgical Anastomosis Rating Scale (MARS10) and a six-stitch test on a latex glove. Statistically significant differences between groups were identified using standard noninferiority analysis, chi-square, and t-tests.
Results Seventy-seven participants completed the course. Baseline test scores did not differ significantly between groups. After the 4-day microsurgical course, both groups showed statistically significant improvement in microsurgical skills measured using the MARS10. The performed tests showed that data for self-learning using digital resources provides noninferior data for course with surpervision on the initial stage of microsurgical training (7.84; standard deviation [SD], 1.92; 95% confidence interval [CI], 7.25–8.44) to (7.72; SD, 2.09; 95% CI, 7.07–8.36).
Conclusion Video-based microsurgical training on its initial step provides noninferior improvement in microsurgical skills to training with a dedicated instructor.
“…Second, the tests did not directly evaluate the manual skills of participants, but only the final product and workflow measured in the time needed to complete the task. 11 42 According to the IMSS consensus, the end-product evaluation tool is sufficiently accurate for basic level courses. 6 Moreover, this study evaluates an intense but short training period (4 days), which is insufficient to achieve microsurgical competency and determine a precise learning curve but provides an introduction of basic microsurgical skills.…”
Background Repetitive training is essential for microsurgical performance. This study aimed to compare the improvement in basic microsurgical skills using two learning methods: stationary microsurgical course with tutor supervision and self-learning based on digital instructional materials. We hypothesized that video-based training provides noninferior improvement in basic microsurgical skills.
Methods In this prospective study, 80 participants with no prior microsurgical experience were randomly divided into two groups: the control group, trained under the supervision of a microsurgical tutor, and the intervention group, where knowledge was based on commonly available online instructional videos without tutor supervision. Three blinded expert microsurgeons evaluated the improvement in basic microsurgical skills in both groups. The evaluation included an end-to-end anastomosis test using the Ten-Point Microsurgical Anastomosis Rating Scale (MARS10) and a six-stitch test on a latex glove. Statistically significant differences between groups were identified using standard noninferiority analysis, chi-square, and t-tests.
Results Seventy-seven participants completed the course. Baseline test scores did not differ significantly between groups. After the 4-day microsurgical course, both groups showed statistically significant improvement in microsurgical skills measured using the MARS10. The performed tests showed that data for self-learning using digital resources provides noninferior data for course with surpervision on the initial stage of microsurgical training (7.84; standard deviation [SD], 1.92; 95% confidence interval [CI], 7.25–8.44) to (7.72; SD, 2.09; 95% CI, 7.07–8.36).
Conclusion Video-based microsurgical training on its initial step provides noninferior improvement in microsurgical skills to training with a dedicated instructor.
“…7 To overcome these barriers, our laboratory has been using three-dimensionally (3D) printed models (eg, intramuscular injection, 8 catheter insertion, 7 intraosseous infusion 9 ) to create customizable simulators affordable for individuals and institutions. Simulation literature shows that to be an effective form of technical skills acquisition, the simulators used do not require the highest levels of realism, 10 especially for trainees, 11 making 3D-printed simulators a feasible option. Interestingly, the main limitation encountered to produce these 3D-printed simulators is the lack of capacity to manufacture the number of simulators needed to support the health professions education curricula.…”
Introduction: Since the catapult of online learning during the COVID-19 pandemic, most simulation laboratories are now completed virtually, leaving a gap in skills training and potential for technical skills decay. Acquiring standard, commercially available simulators is prohibitively expensive, but three-dimensional (3D) printing may provide an alternative. This project aimed to develop the theoretical foundations of a crowdsourcing Web-based application (Web app) to fill the gap in health professions simulation training equipment via community-based 3D printing. We aimed to discover how to effectively leverage crowdsourcing with local 3D printers and use these resources to produce simulators via this Web app accessed through computers or smart devices. Methods: First, a scoping literature review was conducted to discover the theoretical underpinnings of crowdsourcing. Second, these review results were ranked by consumer (health field) and producer (3D printing field) groups via modified Delphi method surveys to determine suitable community engagement strategies for the Web app. Third, the results informed different app iteration ideas and were then generalized beyond the app to address scenarios entailing environmental changes and demands. Results: A scoping review revealed 8 crowdsourcing-related theories. Three were deemed most suitable for our context by both participant groups: Motivation Crowding Theory, Social Exchange Theory, and Transaction Cost Theory. Each theory proposed a different crowdsourcing solution that can streamline additive manufacturing within simulation while applicable to multiple contexts. Conclusions: Results will be aggregated to develop this flexible Web app that adapts to stakeholder needs and ultimately solves this gap by delivering home-based simulation via community mobilization.
“…3 ). 2 , 3 , 4 , 5 There are also many reputable microsurgical university centres that have free technical videos available online for those interested in self-directed learning. Fig.…”
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
