ArticleRadiology case reports2020
Chest CT findings of early and progressive phase COVID-19 infection from a US patient.
Article in Radiology case reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed.
- A cognitive framework based on deep neural network for classification of coronavirus disease.Journal of ambient intelligence and humanized computing · 2022Article
- Pulmonary lymphatic involvement in metastatic uterine sarcomas: imaging and pathological appearance.Radiology case reports · 2021Article
- Medical imaging and computational image analysis in COVID-19 diagnosis: A review.Computers in biology and medicine · 2021Review
- COVIDetection-Net: A tailored COVID-19 detection from chest radiography images using deep learning.Optik · 2021Article
- Fatal pulmonary fibrosis complicating COVID-19 infection in preexistent emphysema.Radiology case reports · 2021Article
- A case report of COVID-19 infection and management during pregnancy.SAGE open medical case reports · 2021Article
- Article
- The importance of standardisation - COVID-19 CT & Radiograph Image Data Stock for deep learning purpose.Computers in biology and medicine · 2020Article
- Consensus transcriptional regulatory networks of coronavirus-infected human cells.Scientific data · 2020Article
- Consensus transcriptional regulatory networks of coronavirus-infected human cells.bioRxiv : the preprint server for biology · 2020Article
- A comparison of COVID-19, SARS and MERS.PeerJ · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The SARS-CoV-2 infection (COVID-19), originally reported in Wuhan, China, has rapidly proliferated throughout several continents and the first case in the United States was reported on January 19, 2020. According to the ACR guidelines issued shortly after this disease was declared a pandemic, radiologists are expected to familiarize themselves with the CT appearance of COVID-19 infection in order to be able to identify specific findings of this entity. This case report discusses the relevant imaging findings of one of the first cases in the mid-western United States. It involves a 60-year-old man who presented with fever, dyspnea, and cough for 1 week and subsequently tested positive for COVID-19. The utility of the noncontrast CT chest in the diagnosis of COVID-19 has been controversial, but there are specific imaging findings that have been increasingly associated with this virus in the appropriate clinical context. The stages of imaging findings in COVID-19 are considered along with the implications of fibrosis throughout the stages. Future considerations include using artificial intelligence algorithms to distinguish between community acquired pneumonias and COVID-19 infection.
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.