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Predicting SIRS after PCNL using machine learning: the joint impact of sarcopenia and staghorn stones.

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Bibliographic Details
Title: Predicting SIRS after PCNL using machine learning: the joint impact of sarcopenia and staghorn stones.
Authors: Wei S; Department of Urology, The Third Affiliated Hospital, Sun Yat-sen University, No. 600 Tianhe Road, Tianhe District, Guangzhou, 510630, Guangdong, P. R. China., Lv B; Department of Urology, The Third Affiliated Hospital, Sun Yat-sen University, No. 600 Tianhe Road, Tianhe District, Guangzhou, 510630, Guangdong, P. R. China., Liu B; Department of Urology, The Third Affiliated Hospital, Sun Yat-sen University, No. 600 Tianhe Road, Tianhe District, Guangzhou, 510630, Guangdong, P. R. China., Huang Q; Department of Urology, The Third Affiliated Hospital, Sun Yat-sen University, No. 600 Tianhe Road, Tianhe District, Guangzhou, 510630, Guangdong, P. R. China., Hu C; Department of Urology, The Third Affiliated Hospital, Sun Yat-sen University, No. 600 Tianhe Road, Tianhe District, Guangzhou, 510630, Guangdong, P. R. China. hucheng2@mail.sysu.edu.cn., Wang H; Department of Urology, The Third Affiliated Hospital, Sun Yat-sen University, No. 600 Tianhe Road, Tianhe District, Guangzhou, 510630, Guangdong, P. R. China. wangh585@mail.sysu.edu.cn.
Source: World journal of urology [World J Urol] 2026 Jun 25; Vol. 44 (1). Date of Electronic Publication: 2026 Jun 25.
Publication Type: Journal Article
Journal Info: Publisher: Springer International Country of Publication: Germany NLM ID: 8307716 Publication Model: Electronic Cited Medium: Internet ISSN: 1433-8726 (Electronic) Linking ISSN: 07244983 NLM ISO Abbreviation: World J Urol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1433-8726
DOI:10.1007/s00345-026-06538-3