Evidence map›Paper›PMID 35515699›Full record

ReviewRSC advances2020

Diagnostic technologies for COVID-19: a review.

Qi Chen, Zhenyun He, Fujing Mao, Hua Pei, Hongmei Cao, Xing Liu

Abstract readReview
In one paragraph

Review in RSC advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 3 pooled it
–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

19 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Rapid, point-of-care antigen tests for diagnosis of SARS-CoV-2 infection.The Cochrane database of systematic reviews · 2022
    Pooled it
  3. Pooled it
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  7. Review
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  16. SARS-CoV-2 origin, myths and diagnostic technology developments.The Egyptian journal of medical human genetics · 2022
    Review
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  18. Review
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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

6 authors.

Qi ChenCollege of Food Science and Engineering, Hainan University Haikou 570228 China qichen@hainanu.edu.cn maofujing11@163.com hmcao@hainanu.edu.cn xliu@hainanu.edu.cn.
Zhenyun HeHainan Institute for Food Control Haikou 570314 China ncuskhezhenyun@163.com.
Fujing MaoCollege of Food Science and Engineering, Hainan University Haikou 570228 China qichen@hainanu.edu.cn maofujing11@163.com hmcao@hainanu.edu.cn xliu@hainanu.edu.cn.
Hua PeiDepartment of Clinical Laboratory, The Second Affiliated Hospital of Hainan Medical University Haikou 570311 China phzmh61@aliyun.com.
Hongmei CaoCollege of Food Science and Engineering, Hainan University Haikou 570228 China qichen@hainanu.edu.cn maofujing11@163.com hmcao@hainanu.edu.cn xliu@hainanu.edu.cn.
Xing LiuCollege of Food Science and Engineering, Hainan University Haikou 570228 China qichen@hainanu.edu.cn maofujing11@163.com hmcao@hainanu.edu.cn xliu@hainanu.edu.cn.ORCID https://orcid.org/0000-0002-1301-4837

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the outbreak of COVID-19 in December 2019, the highly contagious SARS-CoV-2 virus has spread rapidly worldwide. Although the governments across the world have adopted different preventative measures, the spread of the virus still cannot be effectively controlled, and the number of infections and deaths continues to grow. Early diagnosis of COVID-19 is one of the key measures to control the spread of the pandemic and timely treatment of infected people. This review summarizes current COVID-19 diagnostic techniques based on virology, serology, and imaging diagnostics and discusses their advantages and limitations with the aim of providing a reference for rapid and accurate diagnosis of COVID-19.

Identifiers

PMID35515699
PMCPMC9056975

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.