ArticleEndoscopy international open2025
Does development of submucosal dissection models influence quality of training? Comparison of existing models.
Article in Endoscopy international open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Real-world practices and barriers in endoscopic submucosal dissection training: International comprehensive survey.Endoscopy international open · 2026Article
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and study aims: Use of endoscopic submucosal dissection (ESD) is growing, but access to it remains limited. The aim of this study was to compare the performance of various existing models and the progress made by students on them. Methods: Four training models (bovine colon (ex-vivo), ex vivo porcine model, live porcine model, and an artificial model (Endogel)) were evaluated during a 1-week training course. Each participant was evaluated at the beginning (D1) and at the end of the training (D5). Learners performed a standardized ESD of 2 cm on the four models in a randomized order. Experts evaluated the ability of participants to perform ESD using the objective structured assessment of technical skill score (OSATS). Results: Sixteen students were involved, the average age was 35.6 years (+/- 4.6) and they practiced endoscopy for 10 years (+/-5.3). The OSATS significantly increased in each model during the week, with mean scores increasing from 8.6 to 23.3, from 10.7 to 12.9, from 8.8 to 21.3 and from 8.2 to 12.5 for the bovine colon, ex vivo porcine model, live porcine model, and Endogel models, respectively. Conclusions: Ex-vivo models are good models for learning ESD skills. The bovine colon model seems to be the most discriminating. Synthetic models should be reserved for novices.
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