Rodrigo Tejos1, Juan Enrique Berner2,3, Felipe Imigo4,5, Nicolás Besser4, Andrea Ramírez1, Daniel Moreno1, Gonzalo Yañez1, Alvaro Cuadra1, Susana Searle1, Claudio Guerra6. 1. Section of Plastic and Reconstructive Surgery, Surgery Division, School of Medicine, Pontificia Universidad Católica de Chile, Diagonal Paraguay 362, 8330077, Santiago, Chile. 2. Plastic Surgery Department, Royal Victoria Infirmary, Newcastle upon Tyne, UK. 3. Kellogg College, University of Oxford, Oxford, UK. 4. Servicio de Cirugía, Hospital de Puerto Montt, Región de los Lagos, Chile. 5. Facultad de Medicina, Universidad San Sebastian, Lago Panguipulli 1390, 5501842, Puerto Montt, Chile. 6. Section of Plastic and Reconstructive Surgery, Surgery Division, School of Medicine, Pontificia Universidad Católica de Chile, Diagonal Paraguay 362, 8330077, Santiago, Chile. plasticsurgeryuc3@uc.cl.
Abstract
BACKGROUND: Simulation training has become an integral part of plastic surgery postgraduate curricula. It facilitates the acquisition of skills in a safe environment that can be later transferred to real-life settings. A variety of models have been described covering some aspects of the specialty better than others. The aim of this study was to identify and classify all the previously reported plastic surgery simulation models and the possible gaps having the Accreditation Council for Graduate Medical Education (ACGME) list of competencies as a guide. METHODS: Through a Delphi process, the complete list of ACGME minimum requirements for certification was analyzed to identify domains amenable for simulation training. A systematic search was conducted in Pubmed looking for all previously reported simulation models in plastic surgery. Predefined inclusion and exclusion criteria and parallel blind review were used to identify eligible models. RESULTS: A total of 81 ACGME competencies were identified. Following a 3-round Delphi process, consensus was reached on 19 reconstructive and 15 aesthetic surgery domains suitable for simulation training. 1667 articles were initially retrieved from Pubmed, of which 66 articles were eligible for inclusion. Descriptive (65%), quasi-experimental (24%) and experimental studies (11%) were found. For the 34 identified ACGME competencies, there were simulation models described for 58.8% of these, mostly covering reconstructive surgery (84.2%) while for aesthetic surgery it was 13.3%. CONCLUSIONS: This scoping review has identified that there are still gaps in ACGME competencies that could benefit from new simulation training models, especially in those related to aesthetic surgery. LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
BACKGROUND: Simulation training has become an integral part of plastic surgery postgraduate curricula. It facilitates the acquisition of skills in a safe environment that can be later transferred to real-life settings. A variety of models have been described covering some aspects of the specialty better than others. The aim of this study was to identify and classify all the previously reported plastic surgery simulation models and the possible gaps having the Accreditation Council for Graduate Medical Education (ACGME) list of competencies as a guide. METHODS: Through a Delphi process, the complete list of ACGME minimum requirements for certification was analyzed to identify domains amenable for simulation training. A systematic search was conducted in Pubmed looking for all previously reported simulation models in plastic surgery. Predefined inclusion and exclusion criteria and parallel blind review were used to identify eligible models. RESULTS: A total of 81 ACGME competencies were identified. Following a 3-round Delphi process, consensus was reached on 19 reconstructive and 15 aesthetic surgery domains suitable for simulation training. 1667 articles were initially retrieved from Pubmed, of which 66 articles were eligible for inclusion. Descriptive (65%), quasi-experimental (24%) and experimental studies (11%) were found. For the 34 identified ACGME competencies, there were simulation models described for 58.8% of these, mostly covering reconstructive surgery (84.2%) while for aesthetic surgery it was 13.3%. CONCLUSIONS: This scoping review has identified that there are still gaps in ACGME competencies that could benefit from new simulation training models, especially in those related to aesthetic surgery. LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
Authors: Ivan R Diamond; Robert C Grant; Brian M Feldman; Paul B Pencharz; Simon C Ling; Aideen M Moore; Paul W Wales Journal: J Clin Epidemiol Date: 2014-04 Impact factor: 6.437