The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Clinical characteristics of 145 patients with corona virus disease 2019 (COVID-19) in Taizhou, Zhejiang, China
Open accessAbstract
Objective: The aim of this study was to investigate the clinical characteristics of Corona Virus Disease 2019 in Taizhou, China. Methods: A single center retrospective observational study was performed between Jan 1, 2020 and Mar 11, 2020 at Taizhou Public Health Medical Center, Zhejiang, China. All patients with confirmed Corona Virus Disease 2019 were enrolled, and their clinical data were gathered by reviewing electronic medical records. Outcomes of severely ill patients and non-severely ill patients were compared. Results: Of 145 hospitalized patients with COVID-19, the average age was 47.5 years old (standard deviation, 14.6) and 54.5% were men. Hypertension was the most common comorbidity (15.2%), followed by diabetes mellitus (9.7%). Common symptoms included dry cough (81.4%), fever (75.2%), anorexia (42.8%), fatigue (40.7%), chest tightness (32.4%), diarrhea (26.9%) and dizziness (20%). According to imaging examination, 79.3% patients showed bilateral pneumonia, 18.6% showed unilateral pneumonia, 61.4% showed ground-glass opacity, and 2.1% showed no abnormal result. Compared with non-severely ill patients, severely ill patients were older (mean, years, 52.8 vs. 45.3, p < 0.01), had a higher proportion of diabetes mellitus (16.3% vs. 6.9%, p = 0.08), had a higher body mass index (mean, 24.78 vs. 23.20, p = 0.02) and were more likely to have fever (90.7% vs. 68.6%, p = 0.01), anorexia (60.5% vs. 35.3%, p = 0.01), chest tightness (60.5% vs.20.6%, p < 0.01) and dyspnea (7.0% vs. 0%, p = 0.03). Of the 43 severely ill patients, 6 (14%) received high-flow nasal cannula oxygen therapy, and 1 (2.3%) received invasive mechanical ventilation. Conclusions: Older patients or patients with comorbidities such as obesity or diabetes mellitus were more likely to have severe condition. Treatments of COVID-19 is still experimental and more clinical trials are needed.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Self-citationClinical characteristics of coronavirus disease 2019 in China
Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Ou, C.-Q., He, J.-X., Liu, L., Shan, H., Lei, C.-L., Hui, D. S. C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2002032 · Cited by 30,246
Self-citationThe third international consensus definitions for sepsis and septic shock (Sepsis-3).
Singer, M., Deutschman, C. S., Seymour, C. W., Shankar-Hari, M., Annane, D., Bauer, M., Bellomo, R., Bernard, G. R., Chiche, J.-D., Coopersmith, C. M., et al. (2016) · JAMA · doi:10.1001/jama.2016.0287 · Cited by 28,250
A novel coronavirus from patients with pneumonia in China, 2019
Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001017 · Cited by 28,244
Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30211-7 · Cited by 21,415
Self-citationWang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., Wang, B., Xiang, H., Cheng, Z., Xiong, Y., et al. (2020) · JAMA · doi:10.1001/jama.2020.1585 · Cited by 20,148
La certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19
Martínez González, M. (2020) · DSPACE System - Metalibrary (University of the Coast) · Cited by 18,463
Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia
Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K. S. M., Lau, E. H. Y., Wong, J. Y., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001316 · Cited by 16,575
Self-citationChen, Y., Wang, L., Kang, Q., Zhang, X., Yu, G., Wan, X., Wang, J., & Zhu, K. (2017) · Cellular Physiology and Biochemistry · doi:10.1159/000477308 · Cited by 10,679
Self-citationChan, J. F.-W., Yuan, S., Kok, K.-H., To, K. K.-W., Chu, H., Yang, J., Xing, F., Liu, J., Yip, C. C.-Y., Poon, R. W.-S., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30154-9 · Cited by 8,944
Cited by
Pijls, B. G., Jolani, S., Atherley, A., Derckx, R. T., Dijkstra, J. I. R., Franssen, G. H. L., Hendriks, S., Richters, A., Venemans-Jellema, A., Zalpuri, S., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-044640 · Cited by 586
The effect of smoking on COVID‐19 severity: A systematic review and meta‐analysis
Reddy, R. K., Charles, W. N., Sklavounos, A., Dutt, A., Seed, P. T., & Khajuria, A. (2021) · Journal of Medical Virology · doi:10.1002/jmv.26389 · Cited by 458
Pasomsub, E., Watcharananan, S. P., Boonyawat, K., Janchompoo, P., Wongtabtim, G., Suksuwan, W., Sungkanuparph, S., & Phuphuakrat, A. (2021) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2020.05.001 · Cited by 380
Prevalence of obesity among adult inpatients with COVID-19 in France
Caussy, C., Pattou, F., Wallet, F., Simon, C., Chalopin, S., Telliam, C., Mathieu, D., Subtil, F., Frobert, E., Alligier, M., et al. (2020) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(20)30160-1 · Cited by 334
Prevalence of comorbidities among individuals with COVID-19: A rapid review of current literature
Bajgain, K. T., Badal, S., Bajgain, B. B., & Santana, M. J. (2021) · American Journal of Infection Control · doi:10.1016/j.ajic.2020.06.213 · Cited by 328
Metadata
- Authors
- Chen, Q., Zheng, Z., Zhang, C., Zhang, X., Wu, H., Wang, J., Wang, S., & Zheng, C.
- Venue
- Infection
- Published
- 2020-04-28
- Volume
- 48
- Issue
- 4
- Pages
- 543-551
- ISSN
- 0300-8126 (ISSN-L)
- 1439-0973
- OpenAlex
- W3019830619
- Cited by
- 253
- References
- 22
- FWCI
- 7.36
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Clinical characteristics of 145 patients with corona virus disease 2019 (COVID-19) in Taizhou, Zhejiang, China. https://app.cochat.ai/papers/10.1007/s15010-020-01432-5
- The paper
- Chen, Q., Zheng, Z., Zhang, C., Zhang, X., Wu, H., Wang, J., Wang, S., & Zheng, C. (2020). Clinical characteristics of 145 patients with corona virus disease 2019 (COVID-19) in Taizhou, Zhejiang, China. Infection. https://doi.org/10.1007/s15010-020-01432-5
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.