Performance dynamics and learning curve in basic laparoscopic simulation – analysis of 489 participants in a structured training program
DOI:
https://doi.org/10.18203/2349-2902.isj20263338Keywords:
Laparoscopic simulation, Dry lab, Learning curve, Surgical skills, General surgery, Urology, GynecologyAbstract
Background: Laparoscopic surgery requires complex psychomotor skills, including bimanual coordination, adaptation to the fulcrum effect, depth perception, and fine instrument control. Dry-lab simulation enables the development of these skills in a safe, reproducible environment without risk to patients.
Methods: A retrospective, cross-sectional study with convenience sampling was conducted from March 2024 to January 2026 at the Centro de Formación de Mínima Invasión in Mexico City, Mexico, conducted among residents and attending physicians enrolled in a diploma course on advanced laparoscopy and robotic techniques at a minimally invasive surgery training center in Mexico City. Nonparametric tests included Wilcoxon, Friedman, Kruskal-Wallis, and Mann-Whitney U tests, as well as Spearman correlation, with statistical significance set at p<0.05, using statistical package for the social sciences (SPSS) program.
Results: Of 523 initial records, 489 participants were included. Mean age was 30.7±3.7 years; 70.6% were male and 29.4% were female. Most participants belonged to General Surgery (91.0%), followed by Urology (7.0%) and Gynecology (2.0%). Significant improvement was observed in object transfer (35.8%; p<0.001), extracorporeal knot tying (43.5%), and intracorporeal suturing (22.6%; p<0.001). Precision cutting did not show significant improvement: delta +1 second, percentage improvement -0.4%, and p=0.788. Comparisons among tasks demonstrated significant differences (p<0.001).
Conclusions: Structured laparoscopic simulation was associated with significant improvement in three basic tasks. The magnitude of improvement varied according to task type and was greatest for extracorporeal knot tying. Precision cutting requires complementary assessment metrics focused on accuracy and technical quality.
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Copyright (c) 2026 Alejandra Escobar-González, Denisse R. Saavedra-Flores, Victoria A. Sarmiento-Alvarado, Edwin F. Mercado-Pérez, Eduardo Gil-Hurtado, Mariana Barragán-Padilla, David Valadez-Caballero, Federico Ramírez-Madera, Javier Alvarado-Durán, Itzel G. García-Félix

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