Evidence map›Paper›PMID 30854004›Full record

ReviewEvidence-based complementary and alternative medicine : eCAM2019

A Data Mining-Based Analysis of Medication Rules in Treating Bone Marrow Suppression by Kidney-Tonifying Method.

Xing You, Yinkun Xu, Jin Huang, Yaofeng Zhi, Fengzhen Wu, Fuping Ding, Jiaying Liang, Zhihui Cui, Jingyan Xu, Jin Zhang

Open access · hybridAbstract readReview
In one paragraph

Review in Evidence-based complementary and alternative medicine : eCAM, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
5.8field-weighted citation impact, top 5% 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

15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.

  1. Pooled it
  2. Efficacy and Safety ofFrontiers in oncology · 2021
    Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Different Data Mining Approaches Based Medical Text Data.Journal of healthcare engineering · 2021
    Review
  10. A Data Mining-Based Analysis of Core Herbs on Different Patterns (Evidence-based complementary and alternative medicine : eCAM · 2021
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Discovery of Herbal Pairs ContainingEvidence-based complementary and alternative medicine : eCAM · 2020
    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

10 authors at 1 institution in 1 country.

Xing YouSchool of Basic Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, 510006, China.
Yinkun XuSchool of Basic Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, 510006, China.ORCID 0000-0002-9569-568X
Jin HuangSchool of Basic Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, 510006, China.ORCID 0000-0001-8580-6625
Yaofeng ZhiSchool of Basic Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, 510006, China.ORCID 0000-0001-6328-4117
Fengzhen WuSchool of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, 510006, China.
Fuping DingSchool of Nursing, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, 510006, China.
Jiaying LiangSchool of Basic Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, 510006, China.ORCID 0000-0003-1739-9970
Zhihui CuiSchool of Basic Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, 510006, China.
Jingyan XuSchool of Basic Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, 510006, China.
Jin ZhangSchool of Basic Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, 510006, China.ORCID 0000-0003-1224-9455
Guangzhou University of Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo investigate the rule of kidney-tonifying method in Chinese medicine for the treatment of bone marrow suppression (BMS), in order to provide evidence and references for the clinical application of herbs and formulae.

designCollecting and sorting the information about the treatment of BMS related to kidney-tonifying (Bushen) method in Chinese medicine literatures on databases including Chinese National Knowledge Infrastructure (CNKI), and Chinese Biomedical Literature Database (CBM), establishing a database of BMS treating formulae after radiotherapy and chemotherapy with traditional Chinese medicine (TCM) kidney-tonifying method, and finally applying the relevant theories and techniques of data mining to analyze the medication rules of it.

resultsA total of 239 formulae and 202 herbs were included in this database, in which the herbs occurred 2,602 times in general. The high frequency herbs included Astragali Radix (Huangqi), Atractylodis Macrocephalae Rhizoma (Baizhu), and Ligustri Lucidi Fructus (Nvzhenzi). The main herb categories were deficiency-tonifying herbs, blood-activating herbs, dampness-draining diuretic herbs, heat-clearing herbs, and digestant herbs. Deficiency-tonifying herbs accounted for 64.60% of the total number. A total of 8 clustering formulae are summarized according to cluster analysis and 26 herb suits association rules are identified by Apriori algorithm.

conclusionThe treatment of BMS is mainly based on the method of invigorating the spleen and tonifying the kidney and liver to strengthen healthy qi, supplementing with blood-activating herbs, and dampness-draining diuretic herbs to eliminate pathogenic factors.

Identifiers

PMID30854004
PMCPMC6378015
OpenAlexW2915008086

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.