Using Simulation to Define the Learning Curve for the Digital Cervical Examination.

Abstract

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.