Your browser doesn't support javascript.
loading
Acupoint selection rules of acupuncture for cervical spondylotic radiculopathy based on data mining / 中国针灸
Chinese Acupuncture & Moxibustion ; (12): 1259-1262, 2020.
Article in Chinese | WPRIM | ID: wpr-877596
ABSTRACT
OBJECTIVE@#To analyze the rules of acupoint selection in the acupuncture treatment of cervical spondylotic radiculopathy by data mining.@*METHODS@#The randomized controlled trials (RCTs) regarding acupuncture for cervical spondylotic radiculopathy published from July 15 of 2009 to July 15 of 2019 were retrieved from databases of CNKI, VIP, Wanfang, SinoMed, PubMed and EMbase. A database was established with Microsoft Excel 2016. The frequency and total effective rate of high-frequency acupoints, meridians and acupoint combinations were analyzed, and the association rules of acupoints and meridians were analyzed by Apriori algorithm.@*RESULTS@#A total of 87 RCTs were included, involving 104 acupoints with a total frequency of 921. Among them, the high-frequency acupoints were cervical Jiaji (EX-B 2, 87 times), Fengchi (GB 20, 70 times), Houxi (SI 3, 54 times), etc. The frequently-used acupoints were mainly distributed in the hand @*CONCLUSION@#It is feasible to explore the acupoint selection and compatibility rules of acupuncture for cervical spondylotic radiculopathy by data mining. This study could provide corresponding reference for clinical treatment.
Subject(s)

Full text: Available Index: WPRIM (Western Pacific) Main subject: Radiculopathy / Acupuncture Points / Acupuncture Therapy / Meridians / Data Mining Type of study: Controlled clinical trial Limits: Humans Language: Chinese Journal: Chinese Acupuncture & Moxibustion Year: 2020 Type: Article

Similar

MEDLINE

...
LILACS

LIS

Full text: Available Index: WPRIM (Western Pacific) Main subject: Radiculopathy / Acupuncture Points / Acupuncture Therapy / Meridians / Data Mining Type of study: Controlled clinical trial Limits: Humans Language: Chinese Journal: Chinese Acupuncture & Moxibustion Year: 2020 Type: Article