Identification of New Tools to Predict Surgical Performance of Novices using a Plastic Surgery Simulator

To identify new tools capable of predicting surgical performance of novices on an augmentation mammoplasty simulator. The pace of technical skills acquisition varies between residents and may necessitate more time than that allotted by residency training before reaching competence. Identifying applicants with superior innate technical abilities might shorten learning curves and the time to reach competence. The objective of this study is to identify new tools that could predict surgical performance of novices on a mammoplasty simulator.

We recruited 14 medical students and recorded their performance in 2 skill-games: Mikado and Perplexus Epic, and in 2 video games: Star War Racer (Sony Playstation 3) and Super Monkey Ball 2 (Nintendo Wii). Then, each participant performed an augmentation mammoplasty procedure on a Mammoplasty Part-task Trainer, which allows the simulation of the essential steps of the procedure.

The average age of participants was 25.4 years. Correlation studies showed significant association between Perplexus Epic, Star Wars Racer, Super Monkey Ball scores and the modified OSATS score with r_s = 0.8491 (p < 0.001), r_s = -0.6941 (p = 0.005), and r_s = 0.7309 (p < 0.003), but not with the Mikado score r_s = -0.0255 (p = 0.9). Linear regressions were strongest for Perplexus Epic and Super Monkey Ball scores with coefficients of determination of 0.59 and 0.55, respectively. A combined score (Perplexus/Super-Monkey-Ball) was computed and showed a significant correlation with the modified OSATS score having an r_s = 0.8107 (p < 0.001) and R² = 0.75, respectively.

This study identified a combination of skill games that correlated to better performance of novices on a surgical simulator. With refinement, such tools could serve to help screen plastic surgery applicants and identify those with higher surgical performance predictors.