Elif Bilgic1,2, Motaz Alyafi2, Tomonori Hada2, Tara Landry3, Gerald M Fried2, Melina C Vassiliou4. 1. Steinberg Centre for Simulation and Interactive Learning, McGill University, Montreal, QC, Canada. 2. Steinberg-Bernstein Centre for Minimally Invasive Surgery and Innovation, McGill University Health Centre, 1650, Cedar Avenue, L9. 313, Montreal, QC, H3G 1A4, Canada. 3. Montreal General Hospital Medical Library, McGill University Health Centre, Montreal, QC, Canada. 4. Steinberg-Bernstein Centre for Minimally Invasive Surgery and Innovation, McGill University Health Centre, 1650, Cedar Avenue, L9. 313, Montreal, QC, H3G 1A4, Canada. melina.vassiliou@mcgill.ca.
Abstract
BACKGROUND: Laparoscopic suturing (LS) has become a common technique used in a variety of advanced laparoscopic procedures. However, LS is a challenging skill to master, and many trainees may not be competent in performing LS at the end of their training. The purpose of this review is to identify simulation platforms available for assessment of LS skills, and determine the characteristics of the platforms and the LS skills that are targeted. METHODS: A scoping review was conducted between January 1997 and October 2018 for full-text articles. The search was done in various databases. Only articles written in English or French were included. Additional studies were identified through reference lists. The search terms included "laparoscopic suturing" and "clinical competence." RESULTS: Sixty-two studies were selected. The majority of the simulation platforms were box trainers with inanimate tissue, and targeted basic suturing and intracorporeal knot-tying techniques. Most of the validation came from internal structure (rater reliability) and relationship to other variables (compare training levels/case experience, and various metrics). Consequences were not addressed in any of the studies. CONCLUSION: We identified many types of simulation platforms that were used for assessing LS skills, with most being for assessment of basic skills. Platforms assessing the competence of trainees for advanced LS skills were limited. Therefore, future research should focus on development of LS tasks that better reflect the needs of the trainees.
BACKGROUND: Laparoscopic suturing (LS) has become a common technique used in a variety of advanced laparoscopic procedures. However, LS is a challenging skill to master, and many trainees may not be competent in performing LS at the end of their training. The purpose of this review is to identify simulation platforms available for assessment of LS skills, and determine the characteristics of the platforms and the LS skills that are targeted. METHODS: A scoping review was conducted between January 1997 and October 2018 for full-text articles. The search was done in various databases. Only articles written in English or French were included. Additional studies were identified through reference lists. The search terms included "laparoscopic suturing" and "clinical competence." RESULTS: Sixty-two studies were selected. The majority of the simulation platforms were box trainers with inanimate tissue, and targeted basic suturing and intracorporeal knot-tying techniques. Most of the validation came from internal structure (rater reliability) and relationship to other variables (compare training levels/case experience, and various metrics). Consequences were not addressed in any of the studies. CONCLUSION: We identified many types of simulation platforms that were used for assessing LS skills, with most being for assessment of basic skills. Platforms assessing the competence of trainees for advanced LS skills were limited. Therefore, future research should focus on development of LS tasks that better reflect the needs of the trainees.
Authors: Elif Bilgic; Yusuke Watanabe; Dmitry Nepomnayshy; Aimee Gardner; Shimae Fitzgibbons; Iman Ghaderi; Adnan Alseidi; Dimitrios Stefanidis; John Paige; Neal Seymour; Katherine M McKendy; Richard Birkett; James Whitledge; Erica Kane; Nicholas E Anton; Melina C Vassiliou Journal: Am J Surg Date: 2016-10-08 Impact factor: 2.565
Authors: Behnaz Poursartip; Marie-Eve LeBel; Rajni V Patel; Michael D Naish; Ana Luisa Trejos Journal: IEEE Trans Biomed Eng Date: 2017-05-19 Impact factor: 4.538
Authors: Timothy M Kowalewski; Lee W White; Thomas S Lendvay; Iris S Jiang; Robert Sweet; Andrew Wright; Blake Hannaford; Mika N Sinanan Journal: J Surg Res Date: 2014-06-04 Impact factor: 2.192
Authors: Dimitrios Stefanidis; Harsh Grewal; John T Paige; James R Korndorffer; Daniel J Scott; Dmitry Nepomnayshy; David A Edelman; Chris Sievers Journal: Surg Endosc Date: 2014-06-18 Impact factor: 4.584
Authors: Marty Zdichavsky; Martina Krautwald; Maximilian V Feilitzsch; Dörte Wichmann; Alfred Königsrainer; Marc Oliver Schurr Journal: Minim Invasive Ther Allied Technol Date: 2015-12-03 Impact factor: 2.442