METHODS: Urology residents and specialists were invited to test the training model. They were asked to complete a pre-task questionnaire, to perform piecemeal and en bloc resection of 'bladder tumours' within the training model, and to complete a post-task questionnaire afterwards. Their performances were assessed by faculty members of the AUSTEG. For the face validity, a pre-task questionnaire consisting of six statements on TURBT and the training model were set. For the content validity, a post-task questionnaire consisting of 14 items on the details of the training model were set. For the construct validity, a Global Rating Scale was used to assess the participants' performances. The participants were stratified into two groups (junior surgeons and senior surgeons groups) according to their duration of urology training.
RESULTS: For the pre-task questionnaire, a mean score of ≥ 4.0 out of 5.0 was achieved in 5 out of 6 statements. For the post-task questionnaire, a mean score of ≥ 4.5 out of 5.0 was achieved in every item. For the Global Rating Scale, the senior surgeons group had higher scores than the junior surgeons group in 8 out of 11 items as well as the total score.
CONCLUSION: A porcine TURBT training model has been developed, and its face, content and construct validity has been established.
METHODS: The Asian Urological Surgery Training and Educational Group (AUSTEG) Laparoscopic Upper Tract Surgery Course implemented and validated the FLS program for its usage in laparoscopic surgical training. Delegates' basic laparoscopic skills were assessed using three different training models (peg transfer, precision cutting, and intra-corporeal suturing). They also performed live porcine laparoscopic surgery at the same workshop. Live surgery skills were assessed by blinded faculty using the OSATS rating scale.
RESULTS: From March 2016 to March 2019, a total of 81 certified urologists participated in the course, with a median of 5 years of post-residency experience. Although differences in task time did not reach statistical significance, those with more surgical experience were visibly faster at completing the peg transfer and intra-corporeal suturing FLS tasks. However, they took longer to complete the precision cutting task than participants with less experience. Overall OSATS scores correlated weakly with all three FLS tasks (peg transfer time: r=-0.331, r 2=0.110; precision cutting time: r=-0.240, r 2=0.058; suturing with intra-corporeal knot time: r=-0.451, r 2=0.203).
CONCLUSION: FLS task parameters did not correlate strongly with OSATS globing rating scale performance. Although FLS task models demonstrated strong validity, it is important to assimilate the inconsistencies when benchmarking technical proficiency against real-life operative competence, as evaluated by FLS and OSATS, respectively.