METHODS: A total of 152 inexperienced medical students were recruited to a simulation program on labor progression evaluation. After an introductory lecture on basic concepts of labor and birth given by an expert gynecologist, three different gynecologic scenarios were simulated using both a traditional obstetric simulator and the innovative proposed platform, for a total of six tests for each student. A score was assigned for each performed scenario, based on its correctness. Self-assessment questionnaires were compiled before and after the simulation program for additional subjective assessment.
RESULTS: Median score of the simulations performed with our platform was significantly higher than that of the simulations performed with a traditional simulator, for all the three experimented scenarios (P < 0.001).
CONCLUSIONS: The use of a sensorized platform for labor progression allowed for an accurate and faster diagnosis if compared with a traditional simulator even for inexperienced operators, supporting its use in clinical training, which could be realistically introduced into the clinical practice for medical student education.
方法:共有152名没有经验的医学生参加了一项关于劳动进展评估的模拟项目。在妇科医生专家就分娩和分娩的基本概念进行了正面演讲之后,使用传统的产科模拟器和创新的平台模拟了3种不同的妇科方案,每个学生总共进行6次测试。为每个执行的场景分配了一个分数,基于它的正确性。在模拟程序之前和之后编制了自我评估问卷,以进行额外的主观评估。
结果:使用我们的平台进行的模拟的中位数得分明显高于使用传统模拟器进行的模拟之一,对于所有三个实验场景(P<0.001)。
结论:如果与传统模拟器相比,即使对于经验不足的操作员,使用传感器平台进行分娩进展也可以进行准确和更快的诊断。支持将其用作临床培训的有效手段,这可以现实地引入到医学生教育的临床实践中。