Preoperative prediction model for 30 days biliary events after laparoscopic cholecystectomy

doi:10.4240/wjgs.v18.i7.121443

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 18, Issue 7

This Article

(18)

(17)

(0)

(15)

(175)

Peer-Review Report of This Article

CrossCheck and Google Search of This Article

Academic Rules and Norms of This Article

Supplementary Materials of This Article

Citation of this article

Corresponding Author of This Article

Research Domain of This Article

Article-Type of This Article

Open-Access Policy of This Article

Times Cited Counts in Google of This Article

Journal Information of This Article

Publication Name

World Journal of Gastrointestinal Surgery

ISSN

1948-9366

Publisher of This Article

Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Retrospective Cohort Study

Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Gastrointest Surg. Jul 27, 2026; 18(7): 121443
Published online Jul 27, 2026. doi: 10.4240/wjgs.v18.i7.121443

Full Size Figure Download Figure

Figure 3 Forest plot of multivariable logistic regression showing odds ratios and 95%CIs for candidate predictors retained after least absolute shrinkage and selection operator selection.

Citation: Chen ZP. Preoperative prediction model for 30 days biliary events after laparoscopic cholecystectomy. World J Gastrointest Surg 2026; 18(7): 121443
URL: https://www.wjgnet.com/1948-9366/full/v18/i7/121443.htm
DOI: https://dx.doi.org/10.4240/wjgs.v18.i7.121443

Chen ZP. Preoperative prediction model for 30 days biliary events after laparoscopic cholecystectomy. World J Gastrointest Surg 2026; 18(7): 121443 [DOI: 10.4240/wjgs.v18.i7.121443]

All content on this site: Copyright © 1993-2026 Baishideng Publishing Group Inc, its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply.