Validation of the VBLaST pattern cutting task: a learning curve study

Abstract

Background: Mastery of laparoscopic skills is essential in surgical practice and requires considerable time and effort to achieve. The Virtual Basic Laparoscopic Skill Trainer (VBLaST-PC^©) is a virtual simulator that was developed as a computerized version of the pattern cutting (PC) task in the Fundamentals of Laparoscopic Surgery (FLS) system. To establish convergent validity for the VBLaST-PC^©, we assessed trainees' learning curves using the cumulative summation (CUSUM) method and compared them with those on the FLS.

Methods: Twenty-four medical students were randomly assigned to an FLS training group, a VBLaST training group, or a control group. Fifteen training sessions, 30 min in duration per session per day, were conducted over 3 weeks. All subjects completed pretest, posttest, and retention test (2 weeks after posttest) on both the FLS and VBLaST^© simulators. Performance data, including time, error, FLS score, learning rate, learning plateau, and CUSUM score, were analyzed.

Results: The learning curve for all trained subjects demonstrated increasing performance and a performance plateau. CUSUM analyses showed that five of the seven subjects reached the intermediate proficiency level but none reached the expert proficiency level after 150 practice trials. Performance was significantly improved after simulation training, but only in the assigned simulator. No significant decay of skills after 2 weeks of disuse was observed. Control subjects did not show any learning on the FLS simulator, but improved continually in the VBLaST simulator.

Conclusions: Although VBLaST^©- and FLS-trained subjects demonstrated similar learning rates and plateaus, the majority of subjects required more than 150 trials to achieve proficiency. Trained subjects demonstrated improved performance in only the assigned simulator, indicating specificity of training. The virtual simulator may provide better opportunities for learning, especially with limited training exposure.

Keywords: Convergent validity; Cumulative summation (CUSUM); Learning curve; Surgical training; Virtual reality.

Fig. 1

(a) VBLaST-PC (b) FLC-PC (www.flsprogram.org) (c) Comparison of VBLaST-PC on the and FLS-PC on the right

Fig. 1

(a) VBLaST-PC (b) FLC-PC (www.flsprogram.org) (c) Comparison of VBLaST-PC on the and FLS-PC on the right

Fig. 2

Performance group means for the three training groups in pretest, posttest and retention test on the FLS simulator (the error bars represent standard deviation) (a) Completion time (b) Error (c) Normalized Score

Fig. 2

Performance group means for the three training groups in pretest, posttest and retention test on the FLS simulator (the error bars represent standard deviation) (a) Completion time (b) Error (c) Normalized Score

Fig. 3

Performance group means for the three training groups in pretest, posttest and retention test on the VBLaST-PC simulator (the error bars represent standard deviation) (a) Completion time (b) Error (c) Normalized Score

Fig. 3

Performance group means for the three training groups in pretest, posttest and retention test on the VBLaST-PC simulator (the error bars represent standard deviation) (a) Completion time (b) Error (c) Normalized Score

Fig. 4

CUSUM learning curves for medical students trained on FLS simulator using intermediate criterion success score of 56, acceptable failure rate p0=5%

Fig. 5

CUSUM learning curves for medical students trained on FLS simulator using the FLS proficiency criterion success score of 72, acceptable failure rate p0=5%

Fig. 6

CUSUM learning curves for medical students trained on VBLaST-PC simulator using intermediate criterion success score of 56, acceptable failure rate p0=5%

Fig. 7

CUSUM learning curves for medical students trained on VBLaST-PC simulator using the FLS proficiency criterion success score of 72, acceptable failure rate p0=5%

Fig. 8

Inverse curve-fitting for the derivation of learning plateau and learning rate based on (a) normalized FLS scores, and (b) normalized VBLaST scores

Fig. 8

Inverse curve-fitting for the derivation of learning plateau and learning rate based on (a) normalized FLS scores, and (b) normalized VBLaST scores