Evidence map›Paper›PMID 34853598›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2021

Exploratory Compatibility Regularity of Traditional Chinese Medicine on Osteoarthritis Treatment: A Data Mining and Random Walk-Based Identification.

Qiao Zhou, Jian Liu, Ling Xin, Yanyan Fang, Lei Wan, Dan Huang, Jinchen Guo, Jianting Wen, Bing Wang

Open access · hybridAbstract read
In one paragraph

Article in Evidence-based complementary and alternative medicine : eCAM, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
3.8field-weighted citation impact, top 7% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.

  1. Pooled it
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  6. [Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024
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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

9 authors at 3 institutions in 1 country.

Qiao ZhouInstitute of Rheumatism Prevention and Treatment of Traditional Chinese Medicine, Anhui Academy of Chinese Medicine Sciences, Hefei 230031, China.ORCID https://orcid.org/0000-0001-6568-0260
Jian LiuInstitute of Rheumatism Prevention and Treatment of Traditional Chinese Medicine, Anhui Academy of Chinese Medicine Sciences, Hefei 230031, China.ORCID https://orcid.org/0000-0003-3101-7553
Ling XinDepartment of Rheumatism Immunity, The First Affiliated Hospital, Anhui University of Chinese Medicine, Hefei 230031, China.
Yanyan FangInstitute of Rheumatism Prevention and Treatment of Traditional Chinese Medicine, Anhui Academy of Chinese Medicine Sciences, Hefei 230031, China.ORCID https://orcid.org/0000-0001-8754-7786
Lei WanInstitute of Rheumatism Prevention and Treatment of Traditional Chinese Medicine, Anhui Academy of Chinese Medicine Sciences, Hefei 230031, China.
Dan HuangInstitute of Rheumatism Prevention and Treatment of Traditional Chinese Medicine, Anhui Academy of Chinese Medicine Sciences, Hefei 230031, China.
Jinchen GuoInstitute of Rheumatism Prevention and Treatment of Traditional Chinese Medicine, Anhui Academy of Chinese Medicine Sciences, Hefei 230031, China.
Jianting WenInstitute of Rheumatism Prevention and Treatment of Traditional Chinese Medicine, Anhui Academy of Chinese Medicine Sciences, Hefei 230031, China.
Bing WangSchool of Electrical & Informatics Engineering, Anhui University of Technology, Ma'anshan 243002, China.ORCID https://orcid.org/0000-0003-4945-7725
Anhui University of Traditional Chinese Medicine · CNAnhui University · CNAnhui University of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Osteoarthritis (OA) is a degressive and complex disease which is a growing public health problem on a global scale. On basis of an in-house database consisting of clinical records of 13,083 OA patients, the Traditional Chinese Medicine (TCM) was divided into 4 categories of medicines on the basis of the curative properties of herbs. Due to the lack of depth and internal relationship in the calculation results of TCM compatibility law data mining methods such as statistics and frequency analysis, we use a variety of multidimensional complex network methods that can efficaciously find the compatibility law of TCM, including similarity measure, graphical visualization of network diagram, random walking, and propensity score methods. We summarize common couplet medicines utilized for the treatment of osteoarthritis. The similarity measure method was used to investigate the commonly used drugs for the treatment of osteoarthritis. The method of association rule analysis is used to recognize the compatibility between the components. On basis of the propensity score methods, the evaluation displayed that, compared with single drug, the drug group increased ESR, CRP, C3, C4, IgG, and IgA more efficiently. Concluding, a random walk model was constructed to assess drug efficacy. After applying a random walk model, while revealing the compatibility among different components of TCM, their therapeutic efficacy against OA is analyzed. We obtained four groups of drug combination clusters by similarity measure and 11 pairs of highly connected drugs by association rules, which are cardinal drug combinations in the prescription for the treatment of OA. We also found that different traditional drug pairs were associated with different laboratory indexes, and drug combinations could better optimize laboratory indexes. This study presented that the TCM constituents complement one another. Besides, the therapeutic effects resulting from a variety of combinations of these constituents are quite different.

Identifiers

PMID34853598
PMCPMC8629614
OpenAlexW3217398214

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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.