{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Haque TF"],"funding":["National Cancer Institute","NCI NIH HHS","National Institutes of Health"],"pagination":["422-430"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10923136"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["81(3)"],"pubmed_abstract":["<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":["Journal of surgical education"],"pubmed_title":["Competency in Robotic Surgery: Standard Setting for Robotic Suturing Using Objective Assessment and Expert Evaluation."],"pmcid":["PMC10923136"],"funding_grant_id":["R01 CA251579","R01CA251579"],"pubmed_authors":["Hui A","Cen S","Knudsen JE","Ma R","You J","Hung AJ","Goldenberg M","Haque TF","Djaladat H"],"additional_accession":[]},"is_claimable":false,"name":"Competency in Robotic Surgery: Standard Setting for Robotic Suturing Using Objective Assessment and Expert Evaluation.","description":"<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.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Mar","modification":"2025-04-18T21:20:08.58Z","creation":"2025-04-07T09:16:37.499Z"},"accession":"S-EPMC10923136","cross_references":{"pubmed":["38290967"],"doi":["10.1016/j.jsurg.2023.12.002"]}}