ArticleUnited European gastroenterology journal2018
Training in endoscopic mucosal resection and endoscopic submucosal dissection: Face, content and expert validity of the live porcine model.
Article in United European gastroenterology journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 23 citations in OpenAlex.
- Magnetic anchor-guided endoscopic submucosal dissection for colorectal tumors (with video).Surgical endoscopy · 2020Trial
- Novel beagle model of gastric local fibrotic target lesions for the evaluation and training of endoscopic techniques.BMC gastroenterology · 2023Article
- Endoscopic mucosal resection: tips and tricks for gastrointestinal trainees.Translational gastroenterology and hepatology · 2023Review
- How to avoid overtreatment of benign colorectal lesions: Rationale for an evidence-based management.World journal of gastroenterology · 2022Review
- Endoscopic Submucosal Dissection Skills Transfer to Clinical Practice after Hands-On Workshops: An International Survey.Digestive diseases (Basel, Switzerland) · 2022Article
- A Steep Early Learning Curve for Endoscopic Submucosal Dissection in the Live Porcine Model.Digestive diseases (Basel, Switzerland) · 2022Article
- A novel postgraduate endoscopic course using a large animal model of secondary Crohn's disease stricture.Surgical endoscopy · 2021Article
- Task Deconstruction of Colonic Endoscopic Submucosal Dissection (cESD): An Expert Consensus.World journal of surgery · 2020Article
- Current status and trend in training for endoscopic submucosal dissection: A nationwide survey in Korea.PloS one · 2020Article
- Assisted line to visualize endoscopic screen for endoscopic submucosal dissection.VideoGIE : an official video journal of the American Society for Gastrointestinal Endoscopy · 2019Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionEndoscopic mucosal resection and endoscopic submucosal dissection are demanding procedures. This study aims to establish face, content and expert validity of the live porcine model in performing endoscopic mucosal resection, endoscopic submucosal dissection, complication management and to assess it as a training tool. MATERIAL AND
methodsTutors and trainees participating in live porcine model endoscopic mucosal resection and endoscopic submucosal dissection workshops filled out a questionnaire regarding the realism of the model compared to human setting and its role as a learning tool. A 10-point Likert scale was used.
resultsNinety-one endoscopists (13 tutors; 78 trainees) were involved in four workshops. Median global classifications for the realism of the life porcine model ranged between 7.0-8.0 (interquartile range 5.0-9.0). Procedures resembled human cases with a median of 9.0 (8.0-9.0) for oesophageal multiband endoscopic mucosal resection; 8.5 (8.0-9.0) for oesophageal endoscopic submucosal dissection; 9.0 (8.0-10.0) for gastric endoscopic submucosal dissection; and 9.0 (8.5-9.75 and 8.0-9.69) for complication detection and management. The animal model as a learning tool had median scores of 9.0 (7.0-10.0) considering how procedures are performed; 9.0-9.5 (8.0-10.0) for usefulness for beginners; and 9.0-10.0 (5.0-10.0) regarding it a prerequisite.
conclusionsTraining in a live porcine model was considered very realistic compared to the human setting and was highly appreciated as a learning tool. This is the first study to establish face, content and expert validity of the live porcine model in performing multiband endoscopic mucosal resection, oesophageal and gastric endoscopic submucosal dissection. The validation of this model provides the rationale to incorporate it into formal teaching programmes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.