Development and evaluation of a program based on a generative pre-trained transformer model from a public natural language processing platform for efficiency enhancement in post-procedural quality control of esophageal endoscopic submucosal dissection
Author:
Funder
Foundation for Provincial Clinical Key Specialty Development of Shandong Province
Publisher
Springer Science and Business Media LLC
Subject
Surgery
Link
https://link.springer.com/content/pdf/10.1007/s00464-023-10620-x.pdf
Reference31 articles.
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2. Yahagi N (2008) Is esophageal endoscopic submucosal dissection an extreme treatment modality, or can it be a standard treatment modality? Gastrointest Endosc 68:1073–1075
3. Kuttner-Magalhaes R, Dinis-Ribeiro M, Bruno MJ, Marcos-Pinto R, Rolanda C, Koch AD (2022) A steep early learning curve for endoscopic submucosal dissection in the live porcine model. Dig Dis 40:816–825
4. Berr F, Wagner A, Kiesslich T, Friesenbichler P, Neureiter D (2014) Untutored learning curve to establish endoscopic submucosal dissection on competence level. Digestion 89:184–193
5. Ono H, Yao K, Fujishiro M, Oda I, Uedo N, Nimura S, Yahagi N, Iishi H, Oka M, Ajioka Y, Fujimoto K (2021) Guidelines for endoscopic submucosal dissection and endoscopic mucosal resection for early gastric cancer (second edition). Dig Endosc 33:4–20
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