Publication Name	World Journal of Gastrointestinal Surgery
Manuscript ID	94489
Country	Australia

Received	2024-03-19 10:03
Peer-Review Started	2024-03-19 10:03
To Make the First Decision
Return for Revision	2024-06-27 06:09
Revised	2024-07-16 07:02
Second Decision	2024-09-05 02:40
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-09-05 06:44
Articles in Press	2024-09-05 06:44
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-09-19 06:16
Publish the Manuscript Online	2024-09-28 04:34

ISSN	1948-9366 (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/
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Category	Surgery
Manuscript Type	Letter to the Editor
Article Title	Can serious postoperative complications in patients with Crohn’s disease be predicted using machine learning?
Manuscript Source	Invited Manuscript
All Author List	Andrew Paul Zbar
ORCID	Author(s) ORCID Number Andrew Paul Zbar http://orcid.org/0000-0002-0449-4648
Funding Agency and Grant Number
Corresponding Author	Andrew Paul Zbar, FRCS (Ed), MBBS, MD, Doctor, Doctor, Full Professor, Surgeon, Department of Neuroscience and Anatomy, University of Melbourne, Parkville Campus, Grattan Street, Melbourne 3010, Victoria, Australia. apzbar1355@yahoo.com
Key Words	Crohn’s disease; Postoperative complications; Clavien-Dindo; Machine learning; Outcome
Core Tip	Significant postoperative complications continue to be a challenge in those who come to operation for Crohn’s disease. Modern management with immunosuppressive treatment has only significantly delayed surgery rather than prevented the need for operation. Machine learning provides new algorithms that supersede logistic regression of prognostic outcome factors in retrospective analyses. Multi-institutional prospective studies are required to better identify those patients where major complications are likely and where there will be a requirement for postoperative critical care and higher health care expenditure.
Publish Date	2024-09-28 04:34
Citation	<p>Zbar AP. Can serious postoperative complications in patients with Crohn’s disease be predicted using machine learning? <i>World J Gastrointest Surg</i> 2024; 16(10): 3358-3362</p>
URL	https://www.wjgnet.com/1948-9366/full/v16/i10/3358.htm
DOI	https://dx.doi.org/10.4240/wjgs.v16.i10.3358

Full Article (PDF)	WJGS-16-3358-with-cover.pdf
Manuscript File	94489_Auto_Edited_035445.docx
Answering Reviewers	94489-answering-reviewers.pdf
Audio Core Tip	94489-audio.mp3
Conflict-of-Interest Disclosure Form	94489-conflict-of-interest-statement.pdf
Copyright License Agreement	94489-copyright-assignment.pdf
Peer-review Report	94489-peer-reviews.pdf
Scientific Misconduct Check	94489-scientific-misconduct-check.png
Scientific Editor Work List	94489-scientific-editor-work-list.pdf
CrossCheck Report	94489-crosscheck-report.pdf