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1/23/2025 6:19:33 AM | Browse: 62 | Download: 138
Publication Name World Journal of Gastroenterology
Manuscript ID 102071
Country China
Received
2024-10-07 16:13
Peer-Review Started
2024-10-07 16:13
To Make the First Decision
Return for Revision
2024-10-25 03:54
Revised
2024-11-07 17:51
Second Decision
2025-01-03 02:35
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief
2025-01-03 06:20
Articles in Press
2025-01-03 06:20
Publication Fee Transferred
2024-11-09 15:49
Edit the Manuscript by Language Editor
Typeset the Manuscript
2025-01-10 02:09
Publish the Manuscript Online
2025-01-23 05:47
ISSN 1007-9327 (print) and 2219-2840 (online)
Open Access This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Copyright © The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
Article Reprints For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
Permissions For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website http://www.wjgnet.com
Category Surgery
Manuscript Type Retrospective Cohort Study
Article Title Machine learning model-based prediction of postpancreatectomy acute pancreatitis following pancreaticoduodenectomy: A retrospective cohort study
Manuscript Source Unsolicited Manuscript
All Author List Ji-Ming Ma, Peng-Fei Wang, Liu-Qing Yang, Jun-Kai Wang, Jian-Ping Song, Yu-Mei Li, Yan Wen, Bing-Jun Tang and Xue-Dong Wang
Funding Agency and Grant Number
Funding Agency Grant Number
National Natural Science Foundation of China 81930119
Corresponding Author Xue-Dong Wang, MD, Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, No. 168 Litang Road, Changping District, Beijing 102218, China. wxda01026@btch.edu.cn
Key Words Pancreaticoduodenectomy; Postoperative complication; Acute pancreatitis; Machine learning; Predictive value
Core Tip We demonstrate a novel machine learning algorithm for predicting postpancreatectomy acute pancreatitis, achieving excellent performance. The model’s coherent interpretation provides clinical utility in the management and treatment of patients following pancreaticoduodenectomy. This study explains the serious harm caused by such complications and establishes a possible effective preventive measure, providing assistance for clinical work.
Publish Date 2025-01-23 05:47
Citation <p>Ma JM, Wang PF, Yang LQ, Wang JK, Song JP, Li YM, Wen Y, Tang BJ, Wang XD. Machine learning model-based prediction of postpancreatectomy acute pancreatitis following pancreaticoduodenectomy: A retrospective cohort study. <i>World J Gastroenterol</i> 2025; 31(8): 102071</p>
URL https://www.wjgnet.com/1007-9327/full/v31/i8/102071.htm
DOI https://dx.doi.org/10.3748/wjg.v31.i8.102071
Full Article (PDF) WJG-31-102071-with-cover.pdf
STROBE Statement 102071-STROBE-statement.pdf
Manuscript File 102071_Auto_Edited_015719.docx
Answering Reviewers 102071-answering-reviewers.pdf
Audio Core Tip 102071-audio.mp3
Biostatistics Review Certificate 102071-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form 102071-conflict-of-interest-statement.pdf
Copyright License Agreement 102071-copyright-assignment.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s) 102071-foundation-statement.pdf
Signed Informed Consent Form(s) or Document(s) 102071-informed-consent-statement.pdf
Institutional Review Board Approval Form or Document 102071-institutional-review-board-statement.pdf
Non-Native Speakers of English Editing Certificate 102071-non-native-speakers.pdf
Peer-review Report 102071-peer-reviews.pdf
Scientific Misconduct Check 102071-scientific-misconduct-check.png
Scientific Editor Work List 102071-scientific-editor-work-list.pdf
CrossCheck Report 102071-crosscheck-report.pdf