Evidence map›Paper›PMID 33746309›Full record

ArticleScientometrics2021

Analyzing knowledge entities about COVID-19 using entitymetrics.

Qi Yu, Qi Wang, Yafei Zhang, Chongyan Chen, Hyeyoung Ryu, Namu Park, Jae-Eun Baek, Keyuan Li, Yifei Wu, Daifeng Li and 15 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientometrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

25 authors.

Qi YuInstitute of Medical Data Sciences, Shanxi Medical University, Taiyuan, China.
Qi WangInstitute of Medical Data Sciences, Shanxi Medical University, Taiyuan, China.
Yafei ZhangInstitute of Medical Data Sciences, Shanxi Medical University, Taiyuan, China.
Chongyan ChenSchool of Information, University of Texas, Austin, TX USA.
Hyeyoung RyuThe Information School, University of Washington, Seattle, WA USA.
Namu ParkDepartment of Digital Analytics, Yonsei University, Seoul, South Korea.
Jae-Eun BaekDepartment of Library and Information Science, Dae-Gu University, Gyeongsan, South Korea.
Keyuan LiDepartment of Statistics, College of Arts and Sciences, Indiana University, Bloomington, IN USA.
Yifei WuDepartment of Computer Science and Technology, Tsinghua University, Beijing, China.
Daifeng LiSchool of Information Management, Sun Yat-Sen University, Guangzhou, Guangdong China.
Jian XuSchool of Information Management, Sun Yat-Sen University, Guangzhou, Guangdong China.
Meijun LiuInstitute for Global Public Policy, Fudan University, Shanghai, China.
Jeremy J YangDepartment of Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, NM USA.
Chenwei ZhangFaculty of Education, the University of Hong Kong, Hong Kong, China.
Chao LuBusiness School, Hohai University, Nanjing, Jiangsu China.
Peng ZhangDepartment of Computer Science and Technology, Tsinghua University, Beijing, China.
Xin LiSchool of Information Management, Wuhan University, Wuhan, Hubei China.
Baitong ChenDepartment of Library, Information, and Archives, Shanghai University, Shanghai, China.
Islam Akef EbeidSchool of Information, University of Texas, Austin, TX USA.
Julia FenselWestlake High School, Austin, TX USA.
Chao MinSchool of Information Management, Nanjing University, Nanjing, Jiangsu China.
Yujia ZhaiSchool of Information Management, Wuhan University, Wuhan, Hubei China.
Min SongDepartment of Library and Information Science, Yonsei University, Seoul, South Korea.
Ying DingSchool of Information, University of Texas, Austin, TX USA.
Yi BuDepartment of Information Management, Peking University, Beijing, China.ORCID 0000-0003-2549-4580

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 cases have surpassed the 109 + million markers, with deaths tallying up to 2.4 million. Tens of thousands of papers regarding COVID-19 have been published along with countless bibliometric analyses done on COVID-19 literature. Despite this, none of the analyses have focused on domain entities occurring in scientific publications. However, analysis of these bio-entities and the relations among them, a strategy called entity metrics, could offer more insights into knowledge usage and diffusion in specific cases. Thus, this paper presents an entitymetric analysis on COVID-19 literature. We construct an entity-entity co-occurrence network and employ network indicators to analyze the extracted entities. We find that ACE-2 and C-reactive protein are two very important genes and that lopinavir and ritonavir are two very important chemicals, regardless of the results from either ranking.

Indexed as

BibliometricsCOVID-19EntityEntitymetricsKnowledge graphScientific publications

Identifiers

PMID33746309
PMCPMC7953944

What OpenQuestion holds

Textmetadata
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.