摘要
目的:基于《伤寒杂病论》探讨龙骨的应用规律,为临床应用提供借鉴。方法:系统整理《伤寒杂病论》中含有龙骨的条文,采用统计学方法分析各个因素之间的关系。结果:含有龙骨的方剂共7首。对其中的4首汤药方剂进行二元相关性分析显示,龙骨单次用量与龙骨的剂量、药味数量、用水量、剩余水量、每次服用水量和服用次数无相关性,差异无统计学意义(P>0.05)。而且单因素逻辑回归分析显示,龙骨是否为主药与龙骨单次用量、龙骨的剂量、药味数量、用水量、剩余水量、每次服用水量和服用次数无相关性,差异无统计学意义(P>0.05)。4首含有龙骨的方剂中,龙骨单次用量平均为4.43 g,即龙骨的实际服用剂量为7.77~17.73 g(约等于8~18 g)。结论:从量效关系可以发现龙骨的使用不存在显著的量效关系,其用量和功效极为稳定,为临床应用提供了坚实的依据。
Objective: To provide the reference for clinical application of fossil fragments by exploring the application rules of fossil fragments based on ShangHan ZaBing Lun. Methods: The articles containing fossil fragments were systematically collected in ShangHan ZaBing Lun, the relationships between different factors were analyzed by adopting statistical method. Results: There were seven prescriptions containing fossil fragments. Four prescriptions were given bivariate correlation analysis and the results demonstrated that single doses of fossil fragments showed no correlation to the dosages, the numbers of the herbs, the water consumption, residual water, water intake each time and the times of intake, and the difference had no statistical meaning (P〉0.05). While single factor logistic regression analysis presented that whether fossil fragments were the main herbs indicated no correlation to single doses of fossil fragments, the dosage of fossil fragments, the numbers of the herbs, the water consumption, residual water, water intake each time and the times of intake, and the difference had no statistical meaning (P〉0.05). Among four prescriptions, average single dose of fossil fragment was 4.43 g, it meant that actual medication dosage of fossil fragments were 7.77 to 17.73 g (approaching to 8 to 18 g). Conclusion: The notable dose-effect relationship isn't existed in the use of fossil fragments, its usage and the effects are stable, and it could provide solid evidence for clinical application.
出处
《西部中医药》
2017年第8期53-55,共3页
Western Journal of Traditional Chinese Medicine
关键词
龙骨
伤寒论
金匮要略
量效关系
二元相关性分析
单因素逻辑回归分析
fossil fragments
ShangHan Lun
JinGui YaoLve
dose-effect relationship
bivariate correlation analysis
single factor logistic regression analysis