Evidence map›Paper›PMID 37342278›Full record

SynthesisFrontiers in public health2023

Auxiliary screening COVID-19 by computed tomography.

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

Open access · goldAbstract readMeta-AnalysisSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 43% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

7 authors at 4 institutions in 3 countries.

Xiongfeng PanDepartment 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.
Atipatsa C KamingaDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.
Shi Wu WenObstetrics & Maternal Newborn Investigations Research Group, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
Hongying LiuDepartment 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 · CNMzuzu University · MWOttawa 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 the control and prevention of this pandemic. This meta-analysis aims to systematically summarize the current characteristics of the computed tomography (CT) auxiliary screening methods for COVID-19 infection in the real world. Methods: Web of Science, Cochrane Library, Embase, PubMed, CNKI, and Wanfang databases were searched for relevant articles published prior to 1 September 2022. Data on specificity, sensitivity, positive/negative likelihood ratio, area under curve (AUC), and diagnostic odds ratio (dOR) were calculated purposefully. Results: One hundred and fifteen studies were included with 51,500 participants in the meta-analysis. Among these studies, the pooled estimates for AUC of CT in confirmed cases, and CT in suspected cases to predict COVID-19 diagnosis were 0.76 and 0.85, respectively. The CT in confirmed cases dOR was 5.51 (95% CI: 3.78-8.02). The CT in suspected cases dOR was 13.12 (95% CI: 11.07-15.55). Conclusion: Our findings support that CT detection may be the main auxiliary screening method for COVID-19 infection in the real world.

Indexed as

COVID-19COVID-19 TestingHumansSARS-CoV-2Sensitivity and SpecificityTomography, X-Ray Computedcomputed tomographyCOVID-19cross-disciplinary methodsnovel coronavirusnucleic acid detectionreal world

Identifiers

PMID37342278
PMCPMC10278544
OpenAlexW4379467064

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.