R. Farinha, E. Mazzone, Marco Paciotti, Alberto Breda, James Porter, Kris Maes, Ben Van Cleynenbreugel, Jozef Vander Sloten, Alexandre Mottrie, Anthony G. Gallagher
{"title":"肾部分切除术训练模型系统回顾","authors":"R. Farinha, E. Mazzone, Marco Paciotti, Alberto Breda, James Porter, Kris Maes, Ben Van Cleynenbreugel, Jozef Vander Sloten, Alexandre Mottrie, Anthony G. Gallagher","doi":"10.20517/2574-1225.2023.50","DOIUrl":null,"url":null,"abstract":"Robot-assisted partial nephrectomy (PN) is a complex and index procedure with a difficult learning curve that urologists need to learn how to perform safely. We systematically evaluated the development and validation evidence underpinning PN training models (TMs) by extracting and reviewing data from PubMed, Cochrane Library Central, EMBASE, MEDLINE, and Scopus databases from inception to April 2023. The level of evidence was assessed using the Oxford Center for Evidence-Based Medicine. Of the 331 screened articles, 14 cohort studies were included in the analysis. No randomized controlled trials were found, and the heterogeneous nature of the models, study groups, task definitions, and subjectivity of the metrics used were transversal to all studies. All the models were rated good for realism and usefulness as training tools. Methodological discrepancies preclude definitive conclusions regarding the construct validation. No discriminative or predictive validation evidence was reported, nor were there comparisons between an experimental group trained with a TM and a control group. The previous findings stand for the low level of evidence supporting the efficacy of the described TMs in the acquisition of skills required to safely perform PN.","PeriodicalId":388753,"journal":{"name":"Mini-invasive Surgery","volume":"101 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Systematic review on training models for partial nephrectomy\",\"authors\":\"R. Farinha, E. Mazzone, Marco Paciotti, Alberto Breda, James Porter, Kris Maes, Ben Van Cleynenbreugel, Jozef Vander Sloten, Alexandre Mottrie, Anthony G. Gallagher\",\"doi\":\"10.20517/2574-1225.2023.50\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Robot-assisted partial nephrectomy (PN) is a complex and index procedure with a difficult learning curve that urologists need to learn how to perform safely. We systematically evaluated the development and validation evidence underpinning PN training models (TMs) by extracting and reviewing data from PubMed, Cochrane Library Central, EMBASE, MEDLINE, and Scopus databases from inception to April 2023. The level of evidence was assessed using the Oxford Center for Evidence-Based Medicine. Of the 331 screened articles, 14 cohort studies were included in the analysis. No randomized controlled trials were found, and the heterogeneous nature of the models, study groups, task definitions, and subjectivity of the metrics used were transversal to all studies. All the models were rated good for realism and usefulness as training tools. Methodological discrepancies preclude definitive conclusions regarding the construct validation. No discriminative or predictive validation evidence was reported, nor were there comparisons between an experimental group trained with a TM and a control group. The previous findings stand for the low level of evidence supporting the efficacy of the described TMs in the acquisition of skills required to safely perform PN.\",\"PeriodicalId\":388753,\"journal\":{\"name\":\"Mini-invasive Surgery\",\"volume\":\"101 2\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-11-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mini-invasive Surgery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.20517/2574-1225.2023.50\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mini-invasive Surgery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20517/2574-1225.2023.50","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Systematic review on training models for partial nephrectomy
Robot-assisted partial nephrectomy (PN) is a complex and index procedure with a difficult learning curve that urologists need to learn how to perform safely. We systematically evaluated the development and validation evidence underpinning PN training models (TMs) by extracting and reviewing data from PubMed, Cochrane Library Central, EMBASE, MEDLINE, and Scopus databases from inception to April 2023. The level of evidence was assessed using the Oxford Center for Evidence-Based Medicine. Of the 331 screened articles, 14 cohort studies were included in the analysis. No randomized controlled trials were found, and the heterogeneous nature of the models, study groups, task definitions, and subjectivity of the metrics used were transversal to all studies. All the models were rated good for realism and usefulness as training tools. Methodological discrepancies preclude definitive conclusions regarding the construct validation. No discriminative or predictive validation evidence was reported, nor were there comparisons between an experimental group trained with a TM and a control group. The previous findings stand for the low level of evidence supporting the efficacy of the described TMs in the acquisition of skills required to safely perform PN.