Evidence map›Paper›PMID 35111723›Full record

ArticleFrontiers in public health2021

Acceptability of Traditional Chinese Medicine in Chinese People Based on 10-Year's Real World Study With Mutiple Big Data Mining.

Yan Guo, Tengjiao Wang, Wei Chen, Ted J Kaptchuk, Xilian Li, Xiang Gao, Jiahui Yao, Xudong Tang, Ziming Xu

Abstract read
In one paragraph

Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
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

11 citing papers in PubMed.

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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.

Yan GuoXiyuan Hospital Affiliated to China Academy of Chinese Medical Sciences, Beijing, China.
Tengjiao WangBig Data Center for Arts and Sciences, Peking University, Beijing, China.
Wei ChenBig Data Center for Arts and Sciences, Peking University, Beijing, China.
Ted J KaptchukGlobal Health and Social Medicine, Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, MA, United States.
Xilian LiBig Data Center for Arts and Sciences, Peking University, Beijing, China.
Xiang GaoBig Data Center for Arts and Sciences, Peking University, Beijing, China.
Jiahui YaoInstitute of Social Science Survey, Peking University, Beijing, China.
Xudong TangXiyuan Hospital Affiliated to China Academy of Chinese Medical Sciences, Beijing, China.
Ziming XuXiyuan Hospital Affiliated to China Academy of Chinese Medical Sciences, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the past decades, numerous clinical researches have been conducted to illuminate the effects of traditional Chinese medicine for better inheritance and promotion of it, which are mostly clinical trials designed from the doctor's point of view. This large-scale data mining study was conducted from real-world point of view in up to 10 years' big data sets of Traditional Chinese Medicine (TCM) in China, including both medical visits to hospital and cyberspace and contemporaneous social survey data. Finally, some important and interesting findings appear: (1) More Criticisms vs. More Visits. The intensity of criticism increased by 2.33 times over the past 10 years, while the actual number of visits increased by 2.41 times. (2) The people of younger age, highly educated and from economically developed areas have become the primary population for utilizing TCM, which is contrary to common opinions on the characteristics of TCM users. The discovery of this phenomenon indicates that TCM deserves further study on how it treats illness and maintains health.

Indexed as

Big DataMedicine, Chinese TraditionalChinaData MiningHumansbig dataChinadata miningsocial reviewTCMtraditional Chinese medicine

Identifiers

PMID35111723
PMCPMC8802718

What OpenQuestion holds

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Registered trials

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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.