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Articles Published Processes
9/18/2024 11:53:43 AM | Browse: 65 | Download: 164
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Received |
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2024-02-21 18:48 |
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Peer-Review Started |
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2024-02-21 18:48 |
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To Make the First Decision |
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Return for Revision |
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2024-05-18 18:40 |
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Revised |
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2024-05-19 15:35 |
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Second Decision |
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2024-06-14 02:42 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2024-06-14 06:59 |
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Articles in Press |
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2024-06-14 06:59 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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2024-06-16 13:36 |
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Typeset the Manuscript |
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2024-06-20 13:31 |
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Publish the Manuscript Online |
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2024-09-18 11:53 |
| 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/ |
| Copyright |
© The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Surgery |
| Manuscript Type |
Editorial |
| Article Title |
Machine learning as a tool predicting short-term postoperative complications in Crohn’s disease patients undergoing intestinal resection: What frontiers?
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Raffaele Pellegrino and Antonietta Gerarda Gravina |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Antonietta Gerarda Gravina, MD, PhD, Associate Professor, Division of Hepatogastroenterology, Department of Precision Medicine, University of Campania Luigi Vanvitelli, Via L. de Crecchio, Naples 80138, Italy. antoniettagerarda.gravina@unicampania.it |
| Key Words |
Machine learning; Crohn’s disease; Intestinal resection; Postoperative complications; Preoperative assessment; Nutritional optimization; Predictive model; Gastrointestinal surgery; Surgery |
| Core Tip |
In this editorial on the abovementioned study, a machine learning model predicts major postoperative complications within 30 days for Crohn’s disease (CD) patients undergoing intestinal resection. Prioritizing factors include preoperative nutritional status, operative time, and CD activity index. The model’s robustness, with area under the curve values exceeding 0.8, emphasizes the clinical significance of comprehensive preoperative assessment and nutritional optimization in CD. These findings, discussed in the editorial context, align with existing literature and endorse European Society for Clinical Nutrition and Metabolism guidelines. Further research is warranted to refine preoperative strategies for this patient population. |
| Publish Date |
2024-09-18 11:53 |
| Citation |
<p>Pellegrino R, Gravina AG. Machine learning as a tool predicting short-term postoperative complications in Crohn’s disease patients undergoing intestinal resection: What frontiers? <i>World J Gastrointest Surg</i> 2024; 16(9): 2755-2759</p> |
| URL |
https://www.wjgnet.com/1948-9366/full/v16/i9/2755.htm |
| DOI |
https://dx.doi.org/10.4240/wjgs.v16.i9.2755 |
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