Evidence map›Paper›PMID 35140824›Full record

ReviewPolish journal of radiology2022

Computed tomography scan in COVID-19: a systematic review and meta-analysis.

Pouya Mahdavi Sharif, Mehran Nematizadeh, Mahdia Saghazadeh, Amene Saghazadeh, Nima Rezaei

Open access · diamondAbstract readReview
In one paragraph

Review in Polish journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
4.3field-weighted citation impact, top 5% 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

16 citing papers in PubMed, 31 citations in OpenAlex.

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  7. CCL18, CHI3L1, ANG2, IL-6 systemic levels are associated with the extent of lung damage and radiomic features in SARS-CoV-2 infection.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2024
    Article
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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 at 2 institutions in 1 country.

Pouya Mahdavi SharifSystematic Review and Meta-analysis Expert Group (SRMEG), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
Mehran NematizadehSystematic Review and Meta-analysis Expert Group (SRMEG), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
Mahdia SaghazadehSystematic Review and Meta-analysis Expert Group (SRMEG), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
Amene SaghazadehSystematic Review and Meta-analysis Expert Group (SRMEG), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
Nima RezaeiSystematic Review and Meta-analysis Expert Group (SRMEG), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
Tehran University of Medical Sciences · IRUniversal Scientific Education and Research Network · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeComputed tomography (CT) scan is a commonly used tool for the diagnosis of the novel coronavirus disease 2019 (COVID-19), similarly to reverse transcription-polymerase chain reaction (RT-PCR). Because of the limitations of RT-PCR, there is growing interest in the usability of the CT scan. The present systematic review and meta-analysis aims to summarize the available data on the CT scan features of COVID-19. MATERIAL AND

methodsWe conducted a systematic search in electronic databases to find eligible studies published between 1 December 2019 and 4 April 2020, which investigated the computed tomographic features of patients with COVID-19. All preprint and peer-reviewed articles were included. No language limitation was applied. For proportional data, pooled prevalence was calculated using a Freeman-Tukey double arcsine transformation, with a 95% confidence interval (CI).

resultsEighty-six studies were eligible to be included in the meta-analysis. For 7956 patients, the most common CT findings were bilateral pattern of involvement (78%; 95% CI: 0.73-0.82;

conclusionsOur study results show that COVID-19 shares some features with other viral types of pneumonia, despite some differences. They commonly present as GGO along with vascular thickening, air bronchogram and consolidations. Normal CT images, lymphadenopathies, and pleural effusions are not common. Consolidations and pleural effusions correlate with more severe disease. CT features are different between COVID-19 and non-COVID-19 pneumonia. Also, they differ by age, disease severity, and outcomes within COVID-19 patients.

Indexed as

computed tomographyCOVID-19CT scanground-glass opacitiesimagingmeta-analysispaediatricSARS-CoV-2systematic review

Identifiers

PMID35140824
PMCPMC8814899
OpenAlexW4210649515

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.