PURPOSE: This study evaluated face, content, construct validity, and reliability of Thiel embalmed cadavers (TEC) as a training tool for transperitoneal laparoscopic nephrectomy (TLN). MATERIALS AND METHODS: The study participants were prospectively recruited through an advanced laparoscopic renal resection teaching skill course. The participants were grouped into: nonexperts (performed fewer than 50 TLNs) and experts (performed more than 50 TLNs). All the participants performed TLN on TEC, which was video recorded. All participants rated their overall experience of performing TLN on TEC with emphasis on realism (face validity). The participants were asked to rate the appropriateness of TEC for training in TLN (content training). Two experienced laparoscopic surgeons, using validated modified Objective Structured Assessment of Technical Skills, assessed the individual videos. The parameters studied were: Respect for tissues, time and motion, and instrument handling for each step of laparoscopic nephrectomy (LN) (construct validity). Test-retest and interrater reliability was also evaluated. RESULTS: Twenty-four participants (4 experts and 20 nonexperts) took part in this study. The mean overall rating for the experience of TLN on TEC was 4.5 (Face Validity). The mean score for the appropriateness of TEC as a training tool for TLN was 4.6 (content validity) when evaluated by the four experts. The mean scores for the nonexperts and experts using video recordings for various variables were: Respect for tissue-1.74 (standard deviation [SD]-0.62) vs 3.63 (SD-1.06), (P<0.01), time and motion-1.74 (SD-0.58) vs 4 (SD-0.76), (P<0.01), and instrument handling-1.84 (SD-0.61) vs 4.25 (SD-0.71), (P<0.01) (construct validity). The study also suggested test-retest and interrater reliability. CONCLUSIONS: This study suggests face, content, and constructs validity and reliability of the TEC as a training tool for TLN.
PURPOSE: This study evaluated face, content, construct validity, and reliability of Thiel embalmed cadavers (TEC) as a training tool for transperitoneal laparoscopic nephrectomy (TLN). MATERIALS AND METHODS: The study participants were prospectively recruited through an advanced laparoscopic renal resection teaching skill course. The participants were grouped into: nonexperts (performed fewer than 50 TLNs) and experts (performed more than 50 TLNs). All the participants performed TLN on TEC, which was video recorded. All participants rated their overall experience of performing TLN on TEC with emphasis on realism (face validity). The participants were asked to rate the appropriateness of TEC for training in TLN (content training). Two experienced laparoscopic surgeons, using validated modified Objective Structured Assessment of Technical Skills, assessed the individual videos. The parameters studied were: Respect for tissues, time and motion, and instrument handling for each step of laparoscopic nephrectomy (LN) (construct validity). Test-retest and interrater reliability was also evaluated. RESULTS: Twenty-four participants (4 experts and 20 nonexperts) took part in this study. The mean overall rating for the experience of TLN on TEC was 4.5 (Face Validity). The mean score for the appropriateness of TEC as a training tool for TLN was 4.6 (content validity) when evaluated by the four experts. The mean scores for the nonexperts and experts using video recordings for various variables were: Respect for tissue-1.74 (standard deviation [SD]-0.62) vs 3.63 (SD-1.06), (P<0.01), time and motion-1.74 (SD-0.58) vs 4 (SD-0.76), (P<0.01), and instrument handling-1.84 (SD-0.61) vs 4.25 (SD-0.71), (P<0.01) (construct validity). The study also suggested test-retest and interrater reliability. CONCLUSIONS: This study suggests face, content, and constructs validity and reliability of the TEC as a training tool for TLN.
Authors: Patrick Jones; Bhavan Prasad Rai; Hasan A R Qazi; Bhaskar K Somani; Ghulam Nabi Journal: Can Urol Assoc J Date: 2015-12-14 Impact factor: 1.862
Authors: Daniel M Costello; Isabel Huntington; Grace Burke; Brooke Farrugia; Andrea J O'Connor; Anthony J Costello; Benjamin C Thomas; Philip Dundee; Ahmed Ghazi; Niall Corcoran Journal: J Robot Surg Date: 2021-09-03