SynthesisJournal of medical virology2020
CT imaging features of 4121 patients with COVID-19: A meta-analysis.
Synthesis in Journal of medical virology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 9 of them syntheses that pooled 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.
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.
Who cites it
65 citing papers in PubMed, 9 syntheses or guidelines pooled it.
- Do COVID-19 CT features vary between patients from within and outside mainland China? Findings from a meta-analysis.Frontiers in public health · 2022Pooled it
- Computed tomography features of COVID-19 in children: A systematic review and meta-analysis.Medicine · 2021Pooled it
- Typical chest CT features can determine the severity of COVID-19: A systematic review and meta-analysis of the observational studies.Clinical imaging · 2021Pooled it
- Chest CT findings of coronavirus disease 2019 (COVID-19): A comprehensive meta-analysis of 9907 confirmed patients.Clinical imaging · 2021Pooled it
- Chest CT abnormalities in COVID-19: a systematic review.International journal of medical sciences · 2021Pooled it
- Pediatric lung imaging features of COVID-19: A systematic review and meta-analysis.Pediatric pulmonology · 2021Pooled it
- Chest CT findings in asymptomatic cases with COVID-19: a systematic review and meta-analysis.Clinical radiology · 2020Pooled it
- The COVID-19 research landscape: Measuring topics and collaborations using scientific literature.Medicine · 2020Pooled it
- CT imaging features of 4121 patients with COVID-19: A meta-analysis.Journal of medical virology · 2020Pooled it
- Modulation of immune responses by opioids through toll-like and P2X receptor signaling in COVID-19.Purinergic signalling · 2026Review
- Early chest x-ray severity assessed by the MBrixia score is associated with mortality in hospitalized patients with COVID-19 pneumonia.Scientific reports · 2026Article
- The impact of technical factors on the chest radiographic findings in COVID-19 pneumonia.Journal of the colleges of medicine of South Africa · 2026Article
- Incidence of secondary pericardial effusions associated with different etiologies: a comprehensive review of literature.Journal of cardiothoracic surgery · 2025Review
- The role of CT-scan in the diagnosis and prognosis of COVID-19 in a sample of Iraqi patients referred to Al-Diwaniyah Teaching Hospital, Iraq.Journal of medicine and life · 2023Observational
- The Threat of Adversarial Attack on a COVID-19 CT Image-Based Deep Learning System.Bioengineering (Basel, Switzerland) · 2023Article
- Chest x-ray imaging score is associated with severity of COVID-19 pneumonia: the MBrixia score.Scientific reports · 2022Article
- Rapid artificial intelligence solutions in a pandemic-The COVID-19-20 Lung CT Lesion Segmentation Challenge.Medical image analysis · 2022Article
- AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions.Ingenierie et recherche biomedicale : IRBM = Biomedical engineering and research · 2022Review
- Preliminary Guidelines for the Clinical Evaluation and Management of Long COVID.Infection & chemotherapy · 2022Review
- Quantitative Chest CT Analysis to Measure Short-Term Sequelae of COVID-19 Pneumonia: A Monocentric Prospective Study.Tomography (Ann Arbor, Mich.) · 2022Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
objectiveWe systematically reviewed the computed tomography (CT) imaging features of coronavirus disease 2019 (COVID-19) to provide reference for clinical practice.
methodsOur article comprehensively searched PubMed, FMRS, EMbase, CNKI, WanFang databases, and VIP databases to collect literatures about the CT imaging features of COVID-19 from 1 January to 16 March 2020. Three reviewers independently screened literature, extracted data, and assessed the risk of bias of included studies, and then, this meta-analysis was performed by using Stata12.0 software.
resultsA total of 34 retrospective studies involving a total of 4121 patients with COVID-19 were included. The results of the meta-analysis showed that most patients presented bilateral lung involvement (73.8%, 95% confidence interval [CI]: 65.9%-81.1%) or multilobar involvement (67.3%, 95% CI: 54.8%-78.7%) and just little patients showed normal CT findings (8.4%). We found that the most common changes in lesion density were ground-glass opacities (68.1%, 95% CI: 56.9%-78.2%). Other changes in density included air bronchogram sign (44.7%), crazy-paving pattern (35.6%), and consolidation (32.0%). Patchy (40.3%), spider web sign (39.5%), cord-like (36.8%), and nodular (20.5%) were common lesion shapes in patients with COVID-19. Pleural thickening (27.1%) was found in some patients. Lymphadenopathy (5.4%) and pleural effusion (5.3%) were rare.
conclusionThe lung lesions of patients with COVID-19 were mostly bilateral lungs or multilobar involved. The most common chest CT findings were patchy and ground-glass opacities. Some patients had air bronchogram, spider web sign, and cord-like. Lymphadenopathy and pleural effusion were rare.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.