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徐云生治疗甲状腺功能亢进症用药规律的Logistic回归分析 被引量:2

Logistic Regression Analysis of Cases Record of Treating Hyperthyroidism by Professor XU Yun-sheng
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摘要 目的:应用SAS统计软件Logistic功能分析徐云生教授治疗甲亢的用药规律。方法:应用名老中医网收集徐云生教授甲亢医案166份,其他疾病医案194份,建立频率统计表,导出数据,应用SAS统计软件,以甲状腺功能亢进症为因变量,以用药频率>15%的所用中药为自变量,对医案数据集进行Logistic逐步回归分析。结果:赤芍、柴胡、白芍、茯苓等药用药频率较高,Logistic回归分析中柴胡、甘草、丹皮、夏枯草、枸杞、五味子、连翘、川芎、玄参、半夏、泽泻、川牛膝、百合13种药具有统计学意义。结论:徐云生教授治疗甲状腺功能亢进症重视调肝凉血,清热化痰散结,并予以扶正祛邪。本研究结果更具客观性,更具临床指导价值,易被临床医生接受。 Objective: To analyze the prescriptions composition principles of Professor Xu's treating Hyperthyroidism by traditional chinese medicine(TCM). Methods: Input the 166 proven cases record of Professor Xu's to the analysis software of proven cases, and exported data to SAS software, then made the logistic regression analysis of the 40 kinds of drugs which frequency was more than 15%.Results: The frequency of Red peony, Bupleurum, White peony root, Poria were highest. In logistic regression analysis, 13 kinds drugs were statistically significant, including Bupleurum, Licorice, Peony skin, Prunella vulgaris and so on. Conclusion: Through the analysis,Professor Xu's rule of chinese medicine has the following characteristics: firstly, attaching importance to the regulation of liver, blood circulation, attaching importance to the regulation of liver, spleen and kidney, attaching importance to the regulation of spieen and so on.
作者 安文蓉 黄延芹 王经武 徐云生 AN Wen-rong;HUANG Yan-qin;WANG Jing-wu;XU Yun-sheng(The S even-year Maj or of 2011 Enrolling in Shandong University of Traditional Chinese Medicine, Ji' nan250014;The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Ji' nan250014;The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Ji' nan250001)
出处 《实用中西医结合临床》 2018年第1期133-135,共3页 Practical Clinical Journal of Integrated Traditional Chinese and Western Medicine
基金 国家社会科学基金(编号:16BGL181) 山东省医药卫生科技发展计划(编号:2015WS0136) 2015年度中医药行业科研专项(编号:2015468003-2-3)
关键词 甲状腺功能亢进症 徐云生 LOGISTIC回归分析 Hyperthyroidism Professor XU Yun-sheng Logistic regression analysis
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