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Journal Abstract Search

3436 related items for PubMed ID: 29961290

  • 1. [Comparison of machine learning method and logistic regression model in prediction of acute kidney injury in severely burned patients].
    Tang CQ, Li JQ, Xu DY, Liu XB, Hou WJ, Lyu KY, Xiao SC, Xia ZF.
    Zhonghua Shao Shang Za Zhi; 2018 Jun 20; 34(6):343-348. PubMed ID: 29961290
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  • 2. [The features and treatment of Xixia "May 17th" explosion accident].
    Wang S, Yang B, Li X, Sun Y, Cao S, Zhu T, Xu M, Li X, Li T, Zhao J.
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2018 Dec 20; 30(12):1196-1199. PubMed ID: 30592957
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  • 3. [Retrospective study on the myocardial damage of 252 patients with severe burn].
    Zhang C, Zhang J, Zhang D, Xie W, Jiang Z, Lin G, Niu X, Huang Y.
    Zhonghua Shao Shang Za Zhi; 2016 May 20; 32(5):260-5. PubMed ID: 27188483
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  • 8. [Retrospective cohort study on the correlation between high value of lactic acid and risk of death in 127 patients with extensive burn during shock stage].
    Ding XB, Chen J, Yang YT, Peng X, Yan H, Peng YZ.
    Zhonghua Shao Shang Za Zhi; 2019 May 20; 35(5):326-332. PubMed ID: 31154729
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  • 9. [Value of joint prediction model based on the modified systemic inflammatory response syndrome score for predicting mortality risk of patients with large area burns at early stage after admission].
    Fan JH, Sun YF, Wu GS, Wang KA, Wei J, Sun Y.
    Zhonghua Shao Shang Za Zhi; 2020 Jan 20; 36(1):42-47. PubMed ID: 32023717
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  • 10. [Establishment and validation of a risk prediction model for disseminated intravascular coagulation patients with electrical burns].
    Li Q, Ba T, Cao SJ, Chen Q, Zhou B, Yan ZQ, Hou ZH, Wang LF.
    Zhonghua Shao Shang Yu Chuang Mian Xiu Fu Za Zhi; 2023 Aug 20; 39(8):738-745. PubMed ID: 37805784
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  • 11. Clinical characteristics and risk factors for severe burns complicated by early acute kidney injury.
    Chen B, Zhao J, Zhang Z, Li G, Jiang H, Huang Y, Li X.
    Burns; 2020 Aug 20; 46(5):1100-1106. PubMed ID: 31839503
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  • 12. [Analysis on treatment of eight extremely severe burn patients in August 2nd Kunshan factory aluminum dust explosion accident].
    Chai JK, Zheng QY, Li LG, Ye SJ, Wen ZG, Li JJ, Wang SJ, Li DJ, Xie WZ, Wang JL, Hai HL, Chen RJ, Shao JC, Wang H, Li Q, Xu ZM, Xu LP, Xiao HJ, Zhou LM, Feng R.
    Zhonghua Shao Shang Za Zhi; 2018 Jun 20; 34(6):332-338. PubMed ID: 29961288
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  • 13. [Renal echography and cystatin C for prediction of acute kidney injury: very different in patients with cardiac failure or sepsis].
    Zhi H, Zhang M, Cui X, Li Y.
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2019 Oct 20; 31(10):1258-1263. PubMed ID: 31771725
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  • 14. [Establishment of an early risk prediction model for bloodstream infection and analysis of its predictive value in patients with extremely severe burns].
    Zhang Y, Ma ZZ, Wu BW, Dou Y, Zhang Q, Yang LY, Chen EZ.
    Zhonghua Shao Shang Za Zhi; 2021 Jun 20; 37(6):530-537. PubMed ID: 34139830
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  • 16. [A new warning scoring system establishment for prediction of sepsis in patients with trauma in intensive care unit].
    Huang Q, Sun Y, Luo L, Meng S, Chen T, Ai S, Jiang D, Liang H.
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2019 Apr 20; 31(4):422-427. PubMed ID: 31109414
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  • 19. [Comparison of machine learning and Logistic regression model in predicting acute kidney injury after cardiac surgery: data analysis based on MIMIC-III database].
    Xiong W, Zhang L, She K, Xu G, Bai S, Liu X.
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2022 Nov 20; 34(11):1188-1193. PubMed ID: 36567564
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  • 20. [Prognostic significance of plasma gelsolin in severe burn patients with sepsis].
    Huang L, Yao Y, Dong N, He L, Zhang Q, Yu Y, Sheng Z.
    Zhonghua Shao Shang Za Zhi; 2016 Feb 20; 32(2):77-81. PubMed ID: 26902273
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