Evidence map›Paper›PMID 35983367›Full record

SynthesisFrontiers in public health2022

Auxiliary Screening COVID-19 by Serology.

Xiongfeng Pan, Atipatsa C Kaminga, Yuyao Chen, Hongying Liu, Shi Wu Wen, Yingjing Fang, Peng Jia, Aizhong Liu

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in Frontiers in public health, 2022. 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
0.3field-weighted citation impact, top 44% 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, 3 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

8 authors at 3 institutions in 3 countries.

Xiongfeng PanDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.
Atipatsa C KamingaDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.
Yuyao ChenDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.
Hongying LiuDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.
Shi Wu WenOMNI Research Group, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
Yingjing FangDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.
Peng JiaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan, China.
Aizhong LiuDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.
Central South University · CNOttawa Hospital · CAWuhan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The 2019 novel coronavirus (COVID-19) pandemic remains rampant in many countries/regions. Improving the positive detection rate of COVID-19 infection is an important measure for control and prevention of this pandemic. This meta-analysis aims to systematically summarize the current characteristics of the auxiliary screening methods by serology for COVID-19 infection in real world. Methods: Web of Science, Cochrane Library, Embase, PubMed, CNKI, and Wangfang databases were searched for relevant articles published prior to May 1 Results: Sixty-two studies were included with 35,775 participants in the meta-analysis. Among these studies, the pooled estimates for area under the summary receiver operator characteristic of IgG and IgM to predicting COVID-19 diagnosis were 0.974 and 0.928, respectively. The IgG dOR was 209.78 (95% CI: 106.12 to 414.67). The IgM dOR was 78.17 (95% CI: 36.76 to 166.25). Conclusion: Our findings support serum-specific antibody detection may be the main auxiliary screening methods for COVID-19 infection in real world.

Indexed as

COVID-19COVID-19 TestingHumansImmunoglobulin GImmunoglobulin MSensitivity and SpecificityImmunoglobulin GImmunoglobulin MCOVID-19meta-analysisnovel coronavirusnucleic acid detectionserum specific antibody

Identifiers

PMID35983367
PMCPMC9380738
OpenAlexW4289443229

What Socratic 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 Socratic graph.