ArticleJournal of robotic surgery2023
Active control time: an objective performance metric for trainee participation in robotic surgery.
Article in Journal of robotic surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Ruling the robot: a phase-based analysis of resident autonomy across core robotic general surgery procedures.Surgical endoscopy · 2026Article
- Objective performance indicators during robotic Roux-en-Y gastric bypass distinguish experienced from trainee surgeons: a cross-sectional study.Surgical endoscopy · 2025Article
- Automated task-level autonomy assessment in robotic surgery.Surgical endoscopy · 2024Article
- Robotic performance metrics model fellow proficiency in living donor nephrectomy.Journal of robotic surgery · 2024Article
- Is the robotic revolution stunting surgical skills?Surgery open science · 2024Article
- Use of targeted educational resources to improve robotic bariatric surgery training.Surgical endoscopy · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Trainee participation and progression in robotic general surgery remain poorly defined. Computer-assisted technology offers the potential to provide and track objective performance metrics. In this study, we aimed to validate the use of a novel metric-active control time (ACT)-for assessing trainee participation in robotic-assisted cases. Performance data from da Vinci Surgical Systems was retrospectively analyzed for all robotic cases involving trainees with a single minimally invasive surgeon over 10 months. The primary outcome metric was percent ACT-the amount of trainee console time spent in active system manipulations over total active time from both consoles. Kruskal-Wallis and Mann-Whitney U statistical tests were applied in analyses. A total of 123 robotic cases with 18 general surgery residents and 1 fellow were included. Of these, 56 were categorized as complex. Median %ACT was statistically different between trainee levels for all case types taken in aggregate (PGY1s 3.0% [IQR 2-14%], PGY3s 32% [IQR 27-66%], PGY4s 42% [IQR 26-52%], PGY5s 50% [IQR 28-70%], and fellow 61% [IQR 41-85%], p = < 0.0001). When stratified by complexity, median %ACT was higher in standard versus complex cases for PGY5 (60% vs. 36%, p = 0.0002) and fellow groups (74% vs. 47%, p = 0.0045). In this study, we demonstrated an increase in %ACT with trainee level and with standard versus complex robotic cases. These findings are consistent with hypotheses, providing validity evidence for ACT as an objective measurement of trainee participation in robotic-assisted cases. Future studies will aim to define task-specific ACT to guide further robotic training and performance assessments.
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