Publication Name	World Journal of Clinical Cases
Manuscript ID	96215
Country	China

Received	2024-04-29 15:03
Peer-Review Started	2024-04-29 15:03
To Make the First Decision
Return for Revision	2024-05-16 19:45
Revised	2024-05-30 08:27
Second Decision	2024-06-20 02:39
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-06-20 06:55
Articles in Press	2024-06-20 06:55
Publication Fee Transferred
Edit the Manuscript by Language Editor	2024-07-02 18:37
Typeset the Manuscript	2024-07-08 01:34
Publish the Manuscript Online	2024-07-11 09:22

ISSN	2307-8960 (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: https://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
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	Robotics
Manuscript Type	Retrospective Study
Article Title	Application value of machine learning models in predicting intraoperative hypothermia in laparoscopic surgery for polytrauma patients
Manuscript Source	Unsolicited Manuscript
All Author List	Kun Zhu, Zi-Xuan Zhang and Miao Zhang
ORCID	Author(s) ORCID Number Miao Zhang http://orcid.org/0009-0003-5664-0795
Funding Agency and Grant Number
Corresponding Author	Miao Zhang, BSc, N/A, N/A, Department of Internal Medicine, Qingdao Fushan Elderly Apartments, No. 66-68 Jinsong 1st Road, Shibei District, Qingdao 266001, Shandong Province, China. zhangmiaoamiao@163.com
Key Words	Polytrauma; Laparoscopic surgery; Hypothermia; Related factor; Risk prediction
Core Tip	Intraoperative hypothermia is a significant concern during laparoscopic surgery in patients with multiple trauma. This study investigated the value of a machine learning model in predicting hypothermia in this patient population. The results showed that machine learning effectively predicted intraoperative hypothermia, providing a valuable tool to improve surgical safety and patient recovery. Age, baseline temperature, intraoperative temperature, duration of anesthesia, and duration of surgery were identified as independent factors influencing hypothermia. The predictive model had good accuracy and consistency in both the training and validation sets.
Publish Date	2024-07-11 09:22
Citation	<p>Zhu K, Zhang ZX, Zhang M. Application value of machine learning models in predicting intraoperative hypothermia in laparoscopic surgery for polytrauma patients. <i>World J Clin Cases</i> 2024; 12(24): 5513-5522</p>
URL	https://www.wjgnet.com/2307-8960/full/v12/i24/5513.htm
DOI	https://dx.doi.org/10.12998/wjcc.v12.i24.5513

Full Article (PDF)	WJCC-12-5513-with-cover.pdf
Manuscript File	96215-Review-FilipodiaCL.docx
Answering Reviewers	96215-answering-reviewers.pdf
Audio Core Tip	96215-audio.mp3
Biostatistics Review Certificate	96215-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form	96215-conflict-of-interest-statement.pdf
Copyright License Agreement	96215-copyright-assignment.pdf
Signed Informed Consent Form(s) or Document(s)	96215-informed-consent-statement.pdf
Institutional Review Board Approval Form or Document	96215-institutional-review-board-statement.pdf
Non-Native Speakers of English Editing Certificate	96215-non-native-speakers.pdf
Peer-review Report	96215-peer-reviews.pdf
Scientific Misconduct Check	96215-scientific-misconduct-check.png
Scientific Editor Work List	96215-scientific-editor-work-list.pdf
CrossCheck Report	96215-crosscheck-report.pdf
CrossCheck Report	96215-crosscheck-report.png