biostudies-literatureHaque TFNational Cancer InstituteNCI NIH HHSNational Institutes of Health422-430https://www.ebi.ac.uk/biostudies/studies/S-EPMC10923136biostudies-literatureUnknown81(3)<h4>Objective</h4>Surgical skill assessment tools such as the End-to-End Assessment of Suturing Expertise (EASE) can differentiate a surgeon's experience level. In this simulation-based study, we define a competency benchmark for intraoperative robotic suturing using EASE as a validated measure of performance.<h4>Design</h4>Participants conducted a dry-lab vesicourethral anastomosis (VUA) exercise. Videos were each independently scored by 2 trained, blinded reviewers using EASE. Inter-rater reliability was measured with prevalence-adjusted bias-adjusted Kappa (PABAK) using 2 example videos. All videos were reviewed by an expert surgeon, who determined if the suturing skills exhibited were at a competency level expected for residency graduation (pass or fail). The Contrasting Group (CG) method was then used to set a pass/fail score at the intercept of the pass and fail cohorts' EASE score distributions.<h4>Setting</h4>Keck School of Medicine, University of Southern California.<h4>Participants</h4>Twenty-six participants: 8 medical students, 8 junior residents (PGY 1-2), 7 senior residents (PGY 3-5) and 3 attending urologists.<h4>Results</h4>After 1 round of consensus-building, average PABAK across EASE subskills was 0.90 (Range 0.67-1.0). The CG method produced a competency benchmark EASE score of >35/39, with a pass rate of 10/26 (38%); 27% were deemed competent by expert evaluation. False positives and negatives were defined as medical students who passed and attendings who failed the assessment, respectively. This pass/fail score produced no false positives or negatives, and fewer JR than SR were considered competent by both the expert and CG benchmark.<h4>Conclusions</h4>Using an absolute standard setting method, competency scores were set to identify trainees who could competently execute a standardized dry-lab robotic suturing exercise. This standard can be used for high stakes decisions regarding a trainee's technical readiness for independent practice. Future work includes validation of this standard in the clinical environment through correlation with clinical outcomes.Journal of surgical educationCompetency in Robotic Surgery: Standard Setting for Robotic Suturing Using Objective Assessment and Expert Evaluation.PMC10923136R01 CA251579R01CA251579Hui ACen SKnudsen JEMa RYou JHung AJGoldenberg MHaque TFDjaladat HfalseCompetency in Robotic Surgery: Standard Setting for Robotic Suturing Using Objective Assessment and Expert Evaluation.<h4>Objective</h4>Surgical skill assessment tools such as the End-to-End Assessment of Suturing Expertise (EASE) can differentiate a surgeon's experience level. In this simulation-based study, we define a competency benchmark for intraoperative robotic suturing using EASE as a validated measure of performance.<h4>Design</h4>Participants conducted a dry-lab vesicourethral anastomosis (VUA) exercise. Videos were each independently scored by 2 trained, blinded reviewers using EASE. Inter-rater reliability was measured with prevalence-adjusted bias-adjusted Kappa (PABAK) using 2 example videos. All videos were reviewed by an expert surgeon, who determined if the suturing skills exhibited were at a competency level expected for residency graduation (pass or fail). The Contrasting Group (CG) method was then used to set a pass/fail score at the intercept of the pass and fail cohorts' EASE score distributions.<h4>Setting</h4>Keck School of Medicine, University of Southern California.<h4>Participants</h4>Twenty-six participants: 8 medical students, 8 junior residents (PGY 1-2), 7 senior residents (PGY 3-5) and 3 attending urologists.<h4>Results</h4>After 1 round of consensus-building, average PABAK across EASE subskills was 0.90 (Range 0.67-1.0). The CG method produced a competency benchmark EASE score of >35/39, with a pass rate of 10/26 (38%); 27% were deemed competent by expert evaluation. False positives and negatives were defined as medical students who passed and attendings who failed the assessment, respectively. This pass/fail score produced no false positives or negatives, and fewer JR than SR were considered competent by both the expert and CG benchmark.<h4>Conclusions</h4>Using an absolute standard setting method, competency scores were set to identify trainees who could competently execute a standardized dry-lab robotic suturing exercise. This standard can be used for high stakes decisions regarding a trainee's technical readiness for independent practice. Future work includes validation of this standard in the clinical environment through correlation with clinical outcomes.2024-01-01T00:00:00Z2024 Mar2025-04-18T21:20:08.58Z2025-04-07T09:16:37.499ZS-EPMC109231363829096710.1016/j.jsurg.2023.12.002