摘要
目的探求先秦至东汉腹痛证治规律。方法选取该时期具有治疗腹痛功效的方剂,对方剂证型、药物性味归经、药物频次、药物功效类属进行频数统计,并用SPSS Statistics 26.0对高频药物进行聚类分析。结果共选取73方,涉及112味药(归经纳入107味),证候以脾胃虚寒证最为多见,药性以温性、热性为主,药味多为甘味、辛味,药物归经以肺、脾、胃、肝经为主,高频药物之首为白芍,取高频药物前10味进行聚类分析,可聚为4类。结论先秦至东汉时期腹痛证型、用药多样,辨证论治已趋成熟,为后世腹痛的辨治奠定了基础。
Objective To explore the rules of abdominal pain treatment from Pre-Qin to Eastern Han Dynasty.Methods Formulas with the efficacy of treating abdominal pain in this period were selected for the study,and the frequency statistics of formulas,drug flavour and meridian,drug frequency,and drug efficacy categories were conducted,and the cluster analysis of high-frequency drugs was performed with SPSS Statistics 26.0.Results A total of 73 prescriptions were selected,involving 112 flavours(107 flavours were included in the classification of meridians).The symptoms were most common in the spleen and stomach deficiency and cold syndrome,the medicinal properties were mainly warm and hot,and the medicinal flavours were mostly sweet and pungent,and the medicinal meridians were mainly the lung,spleen,stomach,and liver meridians,and the first high-frequency medication was Paeonia lactiflora,and the first 10 flavours were taken for cluster analysis,which could be clustered into four categories.Conclusion During this period,abdominal pain was characterized by a variety of symptoms and medications,and the identification and treatment of abdominal pain had become mature,laying a foundation for the identification and treatment of abdominal pain in later generations.
作者
杨乐
李定祥
YANG Le;LI Dingxiang(The First Clinical College of Traditional Chinese Medicine,Hunan University of Chinese Medicine,Hunan Province,Changsha 410007,China;College of Traditional Chinese Medicine,Hunan University of Chinese Medicine,Hunan Province,Changsha 410208,China)
出处
《光明中医》
2024年第9期1684-1688,共5页
GUANGMING JOURNAL OF CHINESE MEDICINE
基金
湖南省科技创新计划资助(No.2020RC4050)
湖南中医药大学校级科研基金(No.2020XJJJ005)
关键词
腹痛
证治规律
先秦
东汉
文献研究
数据挖掘
abdominal pain
rule for diagnosis and treatment
Pre-Qin Dynasty
Eastern Han Dynasty
document research
data mining