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Updated: Sep 22, 2025

Step By Step: Microsurgical training method combining two nonliving animal models
Published on: May 9, 2015
Pilot Evaluation of a Novel, Low-Cost, Simulation Model for Training and Assessment of Laparoscopic Intracorporeal
Farisa Hossain1,2, Abdulaziz Alnumay1,2, Nawar A Alkhamesi1,2
1CSTAR (Canadian Surgical Technologies & Advanced Robotics), 10033London Health Sciences Centre, London, ON, Canada.
Abstract:
Background: Laparoscopic intracorporeal continuous suturing is being employed in a growing number of minimally invasive procedures. However, there is a lack of adequate bench models for gaining proficiency in this complex task. The purpose of this study was to assess a novel simulation model for running suture. Methods: Participants were grouped as novice (LSN) or expert (LSE) at laparoscopic suturing based on prior experience and training level. A novel low-cost bench model was developed to simulate laparoscopic intracorporeal continuous closure of a defect. The primary outcome measured was time taken to complete the task. Videos were scored by independent raters for Global Operative Assessment of Laparoscopic Skills (GOALS). Results: Sixteen subjects (7 LSE and 9 LSN) participated in this study. LSE completed the task significantly faster than LSN (430 ± 107 vs 637 ± 164 seconds, P ≤ .05). LSN scored higher on accuracy penalties than LSE (Median 30 vs 0, P ≤ .05). Mean GOALS score was significantly different between the 2 groups (LSE 20.64 ± 2.64 vs LSN 14.28 ± 1.94, P < .001) with good inter-rater reliability (ICC ≥ .823). An aggregate score using the formula: Performance Score = 1200-time(sec)-(accuracy penalties x 10) was significantly different between groups with a mean score of 741 ± 141 for LSE vs 285 ± 167 for LSN (P < .001). Conclusion A novel bench model for laparoscopic continuous suturing was able to significantly discriminate between laparoscopic experts and novices. This low-cost model may be useful for both training and assessment of laparoscopic continuous suturing proficiency.
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