Abstract
BackgroundTraining of basic laparoscopic psychomotor skills improves the acquisition of more advanced laparoscopic tasks, such as laparoscopic intra-corporeal knot tying (LICK). This randomized controlled trial was designed to evaluate whether pre-training of basic skills, as laparoscopic camera navigation (LCN), hand-eye coordination (HEC), and bimanual coordination (BMC), and the combination of the three of them, has any beneficial effect upon the learning curve of LICK. The study was carried out in a private center in Asunción, Paraguay, by 80 medical students without any experience in surgery. Four laparoscopic tasks were performed in the ENCILAP model (LCN, HEC, BMC, and LICK). Participants were allocated to 5 groups (G1–G5). The study was structured in 5 phases. In phase 1, they underwent a base-line test (T1) for all tasks (1 repetition of each task in consecutive order). In phase 2, participants underwent different training programs (30 consecutive repetitions) for basic tasks according to the group they belong to (G1: none; G2: LCN; G3: HEC; G4: BMC; and G5: LCN, HEC, and BMC). In phase 3, they were tested again (T2) in the same manner than at T1. In phase 4, they underwent a standardized training program for LICK (30 consecutive repetitions). In phase 5, they were tested again (T3) in the same manner than at T1 and T2. At each repetition, scoring was based on the time taken for task completion system.ResultsThe scores were plotted and non-linear regression models were used to fit the learning curves to one- and two-phase exponential decay models for each participant (individual curves) and for each group (group curves). The LICK group learning curves fitted better to the two-phase exponential decay model. From these curves, the starting points (Y0), the point after HEC training/before LICK training (Y1), the Plateau, and the rate constants (K) were calculated. All groups, except for G4, started from a similar point (Y0). At Y1, G5 scored already better than the others (G1 p = .004; G2 p = .04; G3 p < .0001; G4 NS). Although all groups reached a similar Plateau, G5 has a quicker learning than the others, demonstrated by a higher K (G1 p < 0.0001; G2 p < 0.0001; G3 p < 0.0001; and G4 p < 0.0001).ConclusionsOur data confirms that training improves laparoscopic skills and demonstrates that pre-training of all basic skills (i.e., LCN, HEC, and BMC) shortens the LICK learning curve.
Highlights
Training of basic laparoscopic psychomotor skills improves the acquisition of more advanced laparoscopic tasks, such as laparoscopic intra-corporeal knot tying (LICK)
The results of the basic tasks (LCN, hand-eye coordination (HEC), and bimanual coordination (BMC)) were disregarded for the aims of this publication to avoid the presentation of so many data that could make the understanding of LICK data more confusing and Interest in surgery
For the aims of this and future studies, we developed a novel box trainer model, the ENCILAP model, based on the LASTT model [13] in order to make it more versatile and portable and with a more rigorous and precise design
Summary
Training of basic laparoscopic psychomotor skills improves the acquisition of more advanced laparoscopic tasks, such as laparoscopic intra-corporeal knot tying (LICK). In phase 2, participants underwent different training programs (30 consecutive repetitions) for basic tasks according to the group they belong to (G1: none; G2: LCN; G3: HEC; G4: BMC; and G5: LCN, HEC, and BMC). Laparoscopic surgery demands both surgical and psychomotor skills that not necessarily should be trained together [3, 4], with increasing evidence suggesting that psychomotor skills must be trained earlier and outside the operating room [5,6,7,8,9,10] Following this philosophy, the European Academy of Gynecological Surgery has developed the LASTT (Laparoscopic Skills Training and Testing) model for training basic laparoscopic psychomotor skills, such as laparoscopic camera navigation (LCN), hand-eye coordination (HEC), and bimanual coordination (BMC). The feasibility, face validity, and construct validity of this model have been demonstrated [11,12,13], and together with other tools (i.e., SUTT model, HYSTT model, E-Knot model, The Winner Project [14]), the LASTT model is currently used for certification purposes [3, 4, 15, 16]
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