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Interactive training versus self-driven training in the prediction of colorectal polyp histology by trainees using the NICE classification

BACKGROUND: The COVID-19 pandemic has impacted endoscopic training of the Narrow Band Imaging International Colorectal Endoscopic (NICE) classification, which could accurately predict pathology of colorectal polyps. This study aimed to evaluate the diagnostic performance by trainees of self-driven t...

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Autores principales: Wang, Jia, Qiao, Wei-guang, Ren, Yu-tang, Chen, Yu, Gong, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947896/
https://www.ncbi.nlm.nih.gov/pubmed/36823614
http://dx.doi.org/10.1186/s12876-023-02680-z
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author Wang, Jia
Qiao, Wei-guang
Ren, Yu-tang
Chen, Yu
Gong, Wei
author_facet Wang, Jia
Qiao, Wei-guang
Ren, Yu-tang
Chen, Yu
Gong, Wei
author_sort Wang, Jia
collection PubMed
description BACKGROUND: The COVID-19 pandemic has impacted endoscopic training of the Narrow Band Imaging International Colorectal Endoscopic (NICE) classification, which could accurately predict pathology of colorectal polyps. This study aimed to evaluate the diagnostic performance by trainees of self-driven training vs. interactive training in the prediction of colorectal polyp histology. METHODS: This was a prospective randomized controlled study at five academic centers from January 1, 2021 to May 31, 2021. Trainees with no previous formal training of narrow band imaging or blue light imaging for prediction of colorectal polyp histology were randomly allocated to the self-driven training group or interactive training group. Before and after the training, all trainees were given 20 selected cases of colorectal polyp for testing. Their diagnostic performance was analyzed. RESULTS: Overall, the two training groups showed similar accuracy of NICE classification (79.3% vs. 78.1%; P = 0.637), vessel analysis (77.8% vs. 77.6%, P = 0.939), and surface pattern analysis (78.1% vs. 76.9%, P = 0.616). The accuracy of color analysis in the interactive training group was better (74.4% vs. 80.0%, P = 0.027). For high-confidence predictions, the self-driven training group showed higher accuracy of NICE classification (84.8% vs. 78.7%, P < 0.001) but no difference for analysis of color (79.6% vs. 81.0%), vessel pattern (83.0% vs. 78.5%), and surface pattern (81.8% vs. 78.5%). CONCLUSIONS: Overall, self-driven training showed comparable accuracy of NICE classification, vessel pattern, and surface pattern to interactive training, but lower accuracy of color analysis. This method showed comparable effectiveness and is more applicable than interactive training. It is worth spreading during the COVID-19 pandemic. Trial registration Name of the registry: Chinese Clinical Trial Registry, Trial registration number: ChiCTR2000031659, Date of registration: 06/04/2020, URL of trial registry record: http://www.chictr.org.cn/showproj.aspx?proj=51994
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spelling pubmed-99478962023-02-23 Interactive training versus self-driven training in the prediction of colorectal polyp histology by trainees using the NICE classification Wang, Jia Qiao, Wei-guang Ren, Yu-tang Chen, Yu Gong, Wei BMC Gastroenterol Research BACKGROUND: The COVID-19 pandemic has impacted endoscopic training of the Narrow Band Imaging International Colorectal Endoscopic (NICE) classification, which could accurately predict pathology of colorectal polyps. This study aimed to evaluate the diagnostic performance by trainees of self-driven training vs. interactive training in the prediction of colorectal polyp histology. METHODS: This was a prospective randomized controlled study at five academic centers from January 1, 2021 to May 31, 2021. Trainees with no previous formal training of narrow band imaging or blue light imaging for prediction of colorectal polyp histology were randomly allocated to the self-driven training group or interactive training group. Before and after the training, all trainees were given 20 selected cases of colorectal polyp for testing. Their diagnostic performance was analyzed. RESULTS: Overall, the two training groups showed similar accuracy of NICE classification (79.3% vs. 78.1%; P = 0.637), vessel analysis (77.8% vs. 77.6%, P = 0.939), and surface pattern analysis (78.1% vs. 76.9%, P = 0.616). The accuracy of color analysis in the interactive training group was better (74.4% vs. 80.0%, P = 0.027). For high-confidence predictions, the self-driven training group showed higher accuracy of NICE classification (84.8% vs. 78.7%, P < 0.001) but no difference for analysis of color (79.6% vs. 81.0%), vessel pattern (83.0% vs. 78.5%), and surface pattern (81.8% vs. 78.5%). CONCLUSIONS: Overall, self-driven training showed comparable accuracy of NICE classification, vessel pattern, and surface pattern to interactive training, but lower accuracy of color analysis. This method showed comparable effectiveness and is more applicable than interactive training. It is worth spreading during the COVID-19 pandemic. Trial registration Name of the registry: Chinese Clinical Trial Registry, Trial registration number: ChiCTR2000031659, Date of registration: 06/04/2020, URL of trial registry record: http://www.chictr.org.cn/showproj.aspx?proj=51994 BioMed Central 2023-02-23 /pmc/articles/PMC9947896/ /pubmed/36823614 http://dx.doi.org/10.1186/s12876-023-02680-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Wang, Jia
Qiao, Wei-guang
Ren, Yu-tang
Chen, Yu
Gong, Wei
Interactive training versus self-driven training in the prediction of colorectal polyp histology by trainees using the NICE classification
title Interactive training versus self-driven training in the prediction of colorectal polyp histology by trainees using the NICE classification
title_full Interactive training versus self-driven training in the prediction of colorectal polyp histology by trainees using the NICE classification
title_fullStr Interactive training versus self-driven training in the prediction of colorectal polyp histology by trainees using the NICE classification
title_full_unstemmed Interactive training versus self-driven training in the prediction of colorectal polyp histology by trainees using the NICE classification
title_short Interactive training versus self-driven training in the prediction of colorectal polyp histology by trainees using the NICE classification
title_sort interactive training versus self-driven training in the prediction of colorectal polyp histology by trainees using the nice classification
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947896/
https://www.ncbi.nlm.nih.gov/pubmed/36823614
http://dx.doi.org/10.1186/s12876-023-02680-z
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