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The chart showing PDF series, WORD series, HTML series, Figures (1-5) series, Tables (1-5) series.

Item

Count

PDF

104

WORD

HTML

1491

Figures (1-5)

300

Tables (1-5)

315

Sum=2235

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Item

Count

Browse

181

Download

521

Sum=702

Oct 27, 2023 (publication date) through May 17, 2025

Times Cited of This Article

Times Cited (3)

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Publication Name

World Journal of Gastrointestinal Surgery

ISSN

1948-9366

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

Cited by in CrossRef

For:	Li HY, Zhou JT, Wang YN, Zhang N, Wu SF. Establishment and application of three predictive models of anastomotic leakage after rectal cancer sphincter-preserving surgery. World J Gastrointest Surg 2023; 15(10): 2201-2210 [PMID: 37969722 DOI: 10.4240/wjgs.v15.i10.2201]
URL:	https://www.wjgnet.com/1948-9366/full/v15/i10/2201.htm

Number	Citing Articles
1	Ritch T. J. Geitenbeek, Simon C. Baltus, Mark Broekman, Sander N. Barendsen, Maike C. Frieben, Ilias Asaggau, Elina Thibeau-Sutre, Jelmer M. Wolterink, Matthijs C. Vermeulen, Can O. Tan, Ivo A. M. J. Broeders, Esther C. J. Consten. Multi-Modal Machine Learning for Evaluating the Predictive Value of Pelvimetric Measurements (Pelvimetry) for Anastomotic Leakage After Restorative Low Anterior Resection. Cancers 2025; 17(6): 1051 doi: 10.3390/cancers17061051
2	Paerhati Shayimu, Maitisaidi Awula, Chang-Yong Wang, Rexida Jiapaer, Yi-Peng Pan, Zhi-Min Wu, Yi Chen, Ze-Liang Zhao. Serum nutritional predictive biomarkers and risk assessment for anastomotic leakage after laparoscopic surgery in rectal cancer patients. World Journal of Gastrointestinal Surgery 2024; 16(10): 3142-3154 doi: 10.4240/wjgs.v16.i10.3142 Abstract(346) \| Core Tip(339) \| Full Article(HTML)(1112) \| Full Article with Cover (PDF)-2548K(49) \| Audio-202K(5) \| Peer-Review Report-227K(44) \| Answering Reviewers-143K(45) \| Full Article (PDF)-2094K(64) \| Full Article (XML)-176K(47) \| Times Cited (15) \| Total Visits (3238) \| Open
3	Qianbiao Gu, Yan Xing, Xiaoli Hu, Jiankang Yang, Yong Chen, Yaqiong He, Peng Liu. Interpretable Prognostic Modeling for Postoperative Pancreatic Cancer Using Multi-machine Learning and Habitat Radiomics: A Multi-center Study. Academic Radiology 2025; doi: 10.1016/j.acra.2025.04.025