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Cited by 2,856

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19)

Open access
Pan, Feng, Ye, Tianhe, Sun, Peng, Gui, Shan, Liang, Bo, Li, Lingli, Zheng, Dandan, Wang, Jiazheng, Hesketh, Richard L., Yang, Lian, Zheng, Chuansheng·2020·Radiology

Abstract

= .002); scans obtained in stage 3 (9-13 days) showed consolidation (19 of 21 scans [91%]) and a peak in the total CT score (mean, 7 ± 4); and scans obtained in stage 4 (≥14 days) showed gradual resolution of consolidation (15 of 20 scans [75%]) and a decrease in the total CT score (mean, 6 ± 4) without crazy-paving pattern. Conclusion In patients recovering from coronavirus disease 2019 (without severe respiratory distress during the disease course), lung abnormalities on chest CT scans showed greatest severity approximately 10 days after initial onset of symptoms. © RSNA, 2020.

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Crossref · 10.1148/radiol.2020200370

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Metadata

Authors
Pan, F., Ye, T., Sun, P., Gui, S., Liang, B., Li, L., Zheng, D., Wang, J., Hesketh, R. L., Yang, L., & Zheng, C.
Venue
Radiology
Published
2020-06
Volume
295
Issue
3
Pages
715-721
ISSN
  • 0033-8419 (ISSN-L)
  • 1527-1315
OpenAlex
W3006643024
Cited by
2,856
References
4
FWCI
78.98
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19). https://app.cochat.ai/papers/10.1148/radiol.2020200370
The paper
Pan, F., Ye, T., Sun, P., Gui, S., Liang, B., Li, L., Zheng, D., Wang, J., Hesketh, R. L., Yang, L., & Zheng, C. (2020). Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19). Radiology. https://doi.org/10.1148/radiol.2020200370

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.