Quantifying the “Assistant Effect” in Robotic-Assisted Radical Prostatectomy (RARP): Measures of Technical Performance

Nancy Yu, Hossein Saadat, Antonio Finelli, Jason Y. Lee, Rajiv K. Singal, Teodor P. Grantcharov, Mitchell G. Goldenberg

December 25, 2020. Journal of Surgical Research, Volume 260, pages 307-314.

DOI: https://doi.org/10.1016/j.jss.2020.11.037



Surgeons are reliant on the bedside assistant during robotic surgeries. Using a modified global rating scale (GRS), we aim to assess the association between an assistant’s technical skill on surgeon performance in Robotic-Assisted Radical Prostatectomy (RARP).


Prospective, intraoperative video from RARP cases at three centers were collected. Baseline demographic and RARP-experience data were collected from participating surgeons and trainees. The dissection of the prostatic pedicle and neurovascular bundle step (NVB) was analyzed. Expert analysts scored the console surgeon performance using the Global Evaluative Assessment of Robotic Skills (GEARS), and the bedside assistant performance using a modified Objective Structured Assessment of Technical Skills (aOSATS). The primary outcome is the association between console surgeon performance, as measured by GEARS, and assistant skill, as measured by aOSATS. Spearman’s rho correlations were used to test the relationship between assistant and surgeon technical performance, and a multivariable linear regression model was created to test this association while controlling for patient factors.


92 RARP cases were available for the analysis, comprising 14 console surgeons and 22 different bedside assistants. In only 5 (5.4%) cases, the neurovascular bundle step was completed by a trainee, and in 13 (14.1%) of cases, a staff-level surgeon acted as the bedside assistant. aOSATS score was significantly associated with robotic console experience (P = 0.011), and prior laparoscopic experience (P < 0.001). Assistant aOSATS score showed a weak but significant correlation with surgeon GEARS score during the neurovascular bundle step (spearman’s rho = 0.248, P = 0.028). On linear regression, aOSATS remained a significant predictor of console surgeon performance (P = 0.016), after controlling for patient age and BMI, prostate volume, tumor stage, and presence of nerve-sparing.


This is the first study to assess the association between assistant technical skill and surgeon performance in RARP. Additionally, we have provided validity evidence for a modified OSATS global rating scale for training and assessing bedside assistant performance.