Evidence map›Paper›PMID 31693665›Full record

ArticlePloS one2019

Comparison of drug safety data obtained from the monitoring system, literature, and social media: An empirical proof from a Chinese patent medicine.

Ruixue Hu, Su Golder, Guoyan Yang, Xun Li, Di Wang, Liqiong Wang, Ruyu Xia, Nanqi Zhao, Sainan Fang, Baoyong Lai and 2 more

Open access · goldAbstract readComparative Study
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 35% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Observational
  3. Article
  4. Review
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 3 institutions in 3 countries.

Ruixue HuCentre for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.ORCID 0000-0002-1333-0485
Su GolderDepartment of Health Sciences, University of York, York, England, United Kingdom.
Guoyan YangNICM Health Research Institute, Western Sydney University, Penrith, NSW, Australia.
Xun LiCentre for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Di WangCentre for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Liqiong WangSchool of acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, China.
Ruyu XiaDepartment of Health Sciences, University of York, York, England, United Kingdom.
Nanqi ZhaoCentre for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Sainan FangCentre for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Baoyong LaiCentre for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Jianping LiuCentre for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Yutong FeiCentre for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.ORCID 0000-0003-3051-0581
Beijing University of Chinese Medicine · CNUniversity of York · GBWestern Sydney University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo investigate the consistency of adverse events (AEs) and adverse drug reactions (ADRs) reported in the literature, monitoring and social media data.

methodsUsing one Chinese patent medicine-Cordyceps sinensis extracts (CSE) as an example, we obtained safety data from the national monitoring system (July 2002 to February 2016), literature (up to November 2016) and social media (May 2019). For literature data, we searched the Chinese National Knowledge Infrastructure Database (CNKI), WanFang database, Chinese Science and Technology Periodical Database (VIP), Chinese Biomedical Literature Database (SinoMed), PubMed, Embase and the Cochrane Library. Social media data was from the Baidu post bar and Sina micro-blog. Two authors independently screened the literature and extracted data by PRISMA Harms checklist was followed. AEs and ADRs were coded using the World Health Organization Adverse Reaction Terminology (WHO-ART). AEs and ADRs were grouped into thirty-one organ-system classes for comparisons. Frequencies, relative frequencies and rank were used as metrics. Radar chart was used to manifest the features of the distributions and proportions.

results610 AEs reported in CFDA monitoring data were associated with CSE, of which 537 (88.03%) were suspected ADRs (10.49% certain). 5568 AEs were identified from 172 papers (63% RCTs, 37% other types of studies including case series, case reports, ADR monitoring reports and reviews), in which 86 (1.54%) were ADRs (1.54% certain). 15 AEs (0 certain ADR) were identified from social media. AEs, ADRs and their affected system-organ classes, looked largely similar, but different in every aspect when looking at details. Data from RCTs demonstrated the most disparity.

conclusionsIn our study, the most prevalent AEs and ADRs, mainly gastro-intestinal system disorders including nausea, diarrhea and vomiting, in monitoring system were largely similar with those in literature and social media. But data from different sources varied if looked at details. Multiple data sources (the monitoring system, literature and social media) should be integrated to collect safety information of interventions. The distributions of AEs and ADRs from RCTs were least similar with the data from other sources. Our empirical proof is consistent with other similar studies.

Indexed as

Adverse Drug Reaction Reporting SystemsSocial MediaCordycepsDatabases, PharmaceuticalDrug-Related Side Effects and Adverse ReactionsDrugs, Chinese HerbalGastrointestinal DiseasesHumansPublicationsDrugs, Chinese Herbal

Identifiers

PMID31693665
PMCPMC6834258
OpenAlexW2989130400

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.