Evidence map›Paper›PMID 37309039›Full record

ReviewJournal of Zhejiang University. Science. B2023

Evolution of the newest diagnostic methods for COVID-19: a Chinese perspective.

Mingtao Liu, Jiali Lyu, Xianhui Zheng, Zhiman Liang, Baoying Lei, Huihuang Chen, Yiyin Mai, Huimin Huang, Baoqing Sun

Open access · greenAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of Zhejiang University. Science. B, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 30% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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

9 authors at 2 institutions in 1 country.

Mingtao LiuDepartment of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510140, China.
Jiali LyuNational Center for Respiratory Medicine, National Clinical Research Center of Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510140, China.
Xianhui ZhengDepartment of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510140, China.
Zhiman LiangDepartment of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510140, China.
Baoying LeiGuangzhou Medical University, Guangzhou 511495, China.
Huihuang ChenGuangzhou Medical University, Guangzhou 511495, China.
Yiyin MaiGuangzhou Medical University, Guangzhou 511495, China.
Huimin HuangNational Center for Respiratory Medicine, National Clinical Research Center of Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510140, China. huimin@gird.cn.
Baoqing SunDepartment of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510140, China. sunbaoqing@vip.163.com.
First Affiliated Hospital of Guangzhou Medical University · CNGuangzhou Medical University · CN

Funding

the Emergency Key Project of Guangzhou Laboratory EKPG21-30-2
6 · The paper itself

Abstract

Coronavirus disease 2019 (COVID-19) has continued to spread globally since late 2019, representing a formidable challenge to the world's healthcare systems, wreaking havoc, and spreading rapidly through human contact. With fever, fatigue, and a persistent dry cough being the hallmark symptoms, this disease threatened to destabilize the delicate balance of our global community. Rapid and accurate diagnosis of COVID-19 is a prerequisite for understanding the number of confirmed cases in the world or a region, and an important factor in epidemic assessment and the development of control measures. It also plays a crucial role in ensuring that patients receive the appropriate medical treatment, leading to optimal patient care. Reverse transcription-polymerase chain reaction (RT-PCR) technology is currently the most mature method for detecting viral nucleic acids, but it has many drawbacks. Meanwhile, a variety of COVID-19 detection methods, including molecular biological diagnostic, immunodiagnostic, imaging, and artificial intelligence methods have been developed and applied in clinical practice to meet diverse scenarios and needs. These methods can help clinicians diagnose and treat COVID-19 patients. This review describes the variety of such methods used in China, providing an important reference in the field of the clinical diagnosis of COVID-19.

Indexed as

Artificial IntelligenceCOVID-19ChinaCOVID-19 TestingHumansCoronavirus disease 2019 (COVID-19)DiagnosisImmunoassayPolymerase chain reaction (PCR)RadiographySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

Identifiers

PMID37309039
PMCPMC10264177
OpenAlexW4380422449

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.