Evidence map›Paper›PMID 33273949›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2020

Dose Correlation of Danggui and Chuanxiong Drug Pairs in the Chinese Medicine Prescription Based on the Copula Function.

Xuan Zhao, Wei Lin, Jiawei Li, Yunhui Chen, Anamica Patel, Hailiang Zhao, Guoxin Han, Yiwen Hao, Chaomei Fu, Zejuan Huang and 2 more

Open access · hybridAbstract read
In one paragraph

Article in Evidence-based complementary and alternative medicine : eCAM, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 28% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Dose Correlation ofEvidence-based complementary and alternative medicine : eCAM · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 2 institutions in 3 countries.

Xuan ZhaoSchool of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.ORCID https://orcid.org/0000-0001-5884-2241
Wei LinSchool of Management, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.ORCID https://orcid.org/0000-0002-1360-3455
Jiawei LiSchool of Management, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.ORCID https://orcid.org/0000-0002-6319-3044
Yunhui ChenCollege of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.ORCID https://orcid.org/0000-0002-3555-8018
Anamica PatelObservatory Evidence Service, Public Health Wales, Cardiff CF10 4BZ, UK.ORCID https://orcid.org/0000-0001-9985-4935
Hailiang ZhaoCollege of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.ORCID https://orcid.org/0000-0003-1305-4297
Guoxin HanCollege of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.ORCID https://orcid.org/0000-0003-1495-2291
Yiwen HaoSchool of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.ORCID https://orcid.org/0000-0002-5847-6349
Chaomei FuSchool of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
Zejuan HuangSchool of Management, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.ORCID https://orcid.org/0000-0002-0178-9774
Mingyue ZhengAdelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia.ORCID https://orcid.org/0000-0002-7946-8911
Peng HuSchool of Health and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.ORCID https://orcid.org/0000-0002-4244-0093
Chengdu University of Traditional Chinese Medicine · CNPublic Health Wales · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dosage is essential for studying the compatibility and effectiveness of traditional Chinese medicine. Danggui and Chuanxiong are widely used in traditional Chinese medicine for ailments and treatment of various disorders. 628 traditional Chinese medicine prescriptions containing Danggui and Chuanxiong were extracted from the self-built prescription database and screened for the three groups of prescriptions, i.e., irregular menstruation, sores, and stroke. We processed and tested the dosage of Danggui and Chuanxiong and selected the optimal copula function, Gumbel copula function, from the Archimedes function family and elliptical copula function family to establish the data model. To establish the presence of a correlation between the dose of Danggui and Chuanxiong, a graph of the joint distribution function of rank correlation coefficients, Kendall's rank correlation coefficient and Spearman's rank correlation coefficient, was used. Our results suggest that the model using the Gumbel copula function better reflects the correlation between the dose of Danggui and Chuanxiong. For irregular menstruation, sores, and strokes, Kendall's rank correlation coefficients were 0.6724, 0.5930, and 0.7757, respectively, and Spearman's correlation coefficients were 0.8536, 0.7812, and 0.9285, respectively. In all three prescription groups, the dose of Danggui and Chuanxiong was positively correlated, implying that, as the dosage of one drug increases, the dosage of the other increases as well. From the perspective of data mining and mathematical statistics, the use of the copula function model to evaluate the correlation between the prescribed dosage of the two drugs was innovative and provided a new model for the scientific interpretation of the compatibility of traditional drugs. This might also serve to guide the clinical use of traditional Chinese medicine.

Identifiers

PMID33273949
PMCPMC7700020
OpenAlexW3106920633

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.