Evidence map›Paper›PMID 35093551›Full record

ArticleMicrobes and infection2022

Application experience of a rapid nucleic acid detection system for COVID-19.

Qing Ye, Dezhao Lu, Ting Zhang, Jianhua Mao, Shiqiang Shang

Abstract read
In one paragraph

Article in Microbes and infection, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. 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

5 authors.

Qing YeThe Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, National Children's Regional Medical Center, Hangzhou 310052, China.
Dezhao LuSchool of Life Science, Zhejiang Chinese Medical University, Hangzhou 310053, China.
Ting ZhangSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China.
Jianhua MaoThe Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, National Children's Regional Medical Center, Hangzhou 310052, China. Electronic address: maojh88@zju.edu.cn.
Shiqiang ShangThe Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, National Children's Regional Medical Center, Hangzhou 310052, China. Electronic address: shangsq33@sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is raging worldwide. The COVID-19 outbreak caused severe threats to the life and health of all humans caused by SARS-CoV-2. Clinically, there is an urgent need for an in vitro diagnostic product to detect SARS-CoV-2 nucleic acid quickly. Under this background, commercial SARS-CoV-2 nucleic acid POCT products came into being. However, how to choose these products and how to use these products in a standardized way have brought new puzzles to clinical laboratories. This paper focuses on evaluating the performance of these commercial SARS-CoV-2 nucleic acid POCT products and helps the laboratory make the correct choice. At the same time, to standardize the use of this kind of product, this paper also puts forward corresponding suggestions from six elements of total quality management, namely, human, machine, material, method, environment, and measurement. In addition, this paper also puts forward some ideas on the future development direction of POCT products.

Indexed as

COVID-19Nucleic AcidsCOVID-19 Nucleic Acid TestingHumansSARS-CoV-2Nucleic AcidsCOVID-19Point-of-care testingQuality controlSARS-CoV-2

Identifiers

PMID35093551
PMCPMC8801965

What OpenQuestion holds

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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.