The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Saliva sample as a non-invasive specimen for the diagnosis of coronavirus disease 2019: a cross-sectional study
Open accessAbstract
Objectives Amid the increasing number of pandemic coronavirus disease 2019 (COVID-19) cases, there is a need for a quick and easy method to obtain a non-invasive sample for the detection of this novel coronavirus (severe acute respiratory syndrome coronavirus 2; SARS-CoV-2). We aimed to investigate the potential use of saliva samples as a non-invasive tool for the diagnosis of COVID-19. Methods From 27 March to 4 April 2020, we prospectively collected saliva samples and a standard nasopharyngeal and throat swab in persons seeking care at an acute respiratory infection clinic in a university hospital during the outbreak of COVID-19. Real-time polymerase chain reaction (RT-PCR) was performed, and the results of the two specimens were compared. Results Two-hundred pairs of samples were collected. Sixty-nine (34.5%) individuals were male, and the median (interquartile) age was 36 (28–48) years. Using nasopharyngeal and throat swab RT-PCR as the reference standard, the prevalence of COVID-19 diagnosed by nasopharyngeal and throat swab RT-PCR was 9.5%. The sensitivity and specificity of the saliva sample RT-PCR were 84.2% (95% CI 60.4%–96.6%), and 98.9% (95% CI 96.1%–99.9%), respectively. An analysis of the agreement between the two specimens demonstrated 97.5% observed agreement (κ coefficient 0.851, 95% CI 0.723–0.979; p < 0.001). Conclusions Saliva might be an alternative specimen for the diagnosis of COVID-19. The collection is non-invasive, and non-aerosol generating. This method could facilitate the diagnosis of the disease, given the simplicity of specimen collection and good diagnostic performance.
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
Clinical 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
Hoffmann, M., Kleine-Weber, H., Schroeder, S., Krüger, N., Herrler, T., Erichsen, S., Schiergens, T. S., Herrler, G., Wu, N.-H., Nitsche, A., et al. (2020) · Cell · doi:10.1016/j.cell.2020.02.052 · Cited by 20,286
Interrater reliability: The kappa statistic
McHugh, M. L. (2012) · Biochemia Medica · doi:10.11613/bm.2012.031 · Cited by 19,877
Wu, Z., & McGoogan, J. M. (2020) · JAMA · doi:10.1001/jama.2020.2648 · Cited by 17,016
Chan, 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
Detection of SARS-CoV-2 in Different Types of Clinical Specimens
Wang, W., Xu, Y., Gao, R., Lu, R., Han, K., Wu, G., & Tan, W. (2020) · JAMA · doi:10.1001/jama.2020.3786 · Cited by 5,028
Presumed Asymptomatic Carrier Transmission of COVID-19
Bai, Y., Yao, L., Wei, T., Tian, F., Jin, D.-Y., Chen, L., & Wang, M. (2020) · JAMA · doi:10.1001/jama.2020.2565 · Cited by 4,498
To, K. K.-W., Tsang, O. T.-Y., Leung, W.-S., Tam, A. R., Wu, T.-C., Lung, D. C., Yip, C. C.-Y., Cai, J.-P., Chan, J. M.-C., Chik, T. S.-H., et al. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30196-1 · Cited by 3,066
Xu, X., Chen, P., Wang, J., Feng, J., Zhou, H., Li, X., Zhong, W., & Hao, P. (2020) · Science China Life Sciences · doi:10.1007/s11427-020-1637-5 · Cited by 2,205
Metadata
- Authors
- Pasomsub, E., Watcharananan, S. P., Boonyawat, K., Janchompoo, P., Wongtabtim, G., Suksuwan, W., Sungkanuparph, S., & Phuphuakrat, A.
- Venue
- Clinical Microbiology and Infection
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 27
- Issue
- 2
- Pages
- 285.e1-285.e4
- ISSN
- 1198-743X (ISSN-L)
- OpenAlex
- W3024144282
- Cited by
- 380
- References
- 16
- FWCI
- 10.59
- Type
- journal article
- Language
- en
- Open access
- bronze
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: Saliva sample as a non-invasive specimen for the diagnosis of coronavirus disease 2019: a cross-sectional study. https://app.cochat.ai/papers/10.1016/j.cmi.2020.05.001
- The paper
- Pasomsub, E., Watcharananan, S. P., Boonyawat, K., Janchompoo, P., Wongtabtim, G., Suksuwan, W., Sungkanuparph, S., & Phuphuakrat, A. (2021). Saliva sample as a non-invasive specimen for the diagnosis of coronavirus disease 2019: a cross-sectional study. Clinical Microbiology and Infection. https://doi.org/10.1016/j.cmi.2020.05.001
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.