Departments of Obstetrics and Gynecology and Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina.
Obstet Gynecol. 2019 Oct;134 Suppl 1:1S-8S. doi: 10.1097/AOG.0000000000003433.
To assess how skill in the digital cervical examination is acquired in medical students.
In a longitudinal study, medical students completed 200 simulated cervical examinations. We performed regressions of each student's cumulative accuracy using the linear, power, and sigmoidal models to determine best fit. We also constructed multilevel models to determine the influence of dilation and effacement on accuracy and to determine whether the starting level and rate of learning varied between individuals. To assess skill decay, we assessed accuracy at 1, 2, and 5 months after training. We defined the amount of sustained accuracy needed to achieve competence using cumulative summation analyses and determined the amount of practice needed to reach this level of skill.
Twenty-five medical students participated. The median (interquartile range) of cumulative accuracy at the end of the study was 69% (65-78) for dilation and 80% (76-91) for effacement. The sigmoidal model had the best fit. All students achieved competence during the study. The multilevel models showed that accuracy decreased with higher dilation and lower effacement and found that starting level and rate of learning varied between individuals. Maximal accuracy in both dilation and effacement was seen after 150 repetitions. Accuracy of the medical students persisted for 1 month for dilation and 2 months for effacement. The average±SD number of repetitions needed to achieve competence was 89±46 (range 35-195) for dilation and 48±38 (range 11-174) for effacement.
Based on the variability in skill between individuals and the rate of skill acquisition and decay, we feel that a competence-based rather than time-based approach is most appropriate, that trainee performance should be monitored both during and after training, and that 150 repetitions, or more, should be included in any digital cervical examination simulation regimen.
评估医学生如何获得数字宫颈检查技能。
在一项纵向研究中，医学生完成了 200 次模拟宫颈检查。我们使用线性、幂和 S 型模型对每个学生的累积准确率进行回归，以确定最佳拟合。我们还构建了多层模型，以确定扩张和消退对准确性的影响，并确定个体之间的起始水平和学习速度是否存在差异。为了评估技能衰减，我们在培训后 1、2 和 5 个月评估了准确性。我们使用累积和分析来确定达到熟练水平所需的持续准确性量，并确定达到该技能水平所需的练习量。
25 名医学生参与了研究。研究结束时，累积准确率的中位数（四分位距）为扩张时 69%（65-78），消退时 80%（76-91）。S 型模型具有最佳拟合度。所有学生在研究期间都达到了熟练水平。多层模型表明，准确性随扩张程度增加和消退程度降低而降低，并发现个体之间起始水平和学习速度存在差异。在 150 次重复后，扩张和消退的准确率均达到最大值。扩张和消退的准确率在 1 个月和 2 个月时均保持不变。达到熟练水平所需的平均±SD 重复次数分别为扩张时 89±46（范围 35-195）和消退时 48±38（范围 11-174）。
基于个体之间技能的可变性以及技能获得和衰减的速度，我们认为基于能力而不是基于时间的方法最合适，培训师的表现应该在培训期间和之后进行监测，并且应该包括在任何数字宫颈检查模拟方案中，重复 150 次或更多次。
