Laparoscopic Simulation Training Improves Operating Room Performance of Surgical Residents: a Multicenter Randomized Trial (NOVICE).
International journal of surgery (London, England)(2025)
Abstract
BACKGROUND:Learning basic laparoscopic skills can be efficiently performed ex vivo in a safe environment using simulation devices. However, in many countries, the broad and mandatory implementation of ex vivo laparoscopic simulation training in surgical residency is still lacking. The aim of the study was to evaluate the efficacy of laparoscopic basic skills simulation training on the surgical performance of residents during their first laparoscopic procedures in the operating room. METHODS:This was a multicenter, prospective, randomized, two-arm, single-blind trial. The study recruited first-year surgical residents (NOVICE) with no previous personal experience in laparoscopic surgery. After the participants had performed their first laparoscopic cholecystectomy, they were randomized into two groups: the intervention group underwent six weeks of standardized laparoscopic basic skills simulation training (Lübeck Toolbox Curriculum), whereas the control group did not. After six weeks, both groups performed a second cholecystectomy. The videotapes of the first and second cholecystectomies were evaluated blinded based on the Global Operative Assessment of Laparoscopic Skill (GOALS) score. The primary endpoint was the changes in the GOALS scores between the first and second cholecystectomies. RESULTS:22 surgical residents from 11 surgical clinics in Germany were included, and 4 residents dropped out. The median improvement in the LTB-Curriculum group between CHE I and CHE II was 8.5 GOALS score points in contrast to 2 points in the control group. This difference was statistically significant (95%CI: 1-15 points, P = 0.013). CONCLUSION:Ex-vivo training in basic laparoscopic skills significantly improved the surgical performance of residents during their first laparoscopic cholecystectomies in the operating room.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined