The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Potential preanalytical and analytical vulnerabilities in the laboratory diagnosis of coronavirus disease 2019 (COVID-19)
Open accessAbstract
A novel zoonotic coronavirus outbreak is spreading all over the world. This pandemic disease has now been defined as novel coronavirus disease 2019 (COVID-19), and is sustained by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As the current gold standard for the etiological diagnosis of SARS-CoV-2 infection is (real time) reverse transcription polymerase chain reaction (rRT-PCR) on respiratory tract specimens, the diagnostic accuracy of this technique shall be considered a foremost prerequisite. Overall, potential RT-PCR vulnerabilities include general preanalytical issues such as identification problems, inadequate procedures for collection, handling, transport and storage of the swabs, collection of inappropriate or inadequate material (for quality or volume), presence of interfering substances, manual errors, as well as specific aspects such as sample contamination and testing patients receiving antiretroviral therapy. Some analytical problems may also contribute to jeopardize the diagnostic accuracy, including testing outside the diagnostic window, active viral recombination, use of inadequately validated assays, insufficient harmonization, instrument malfunctioning, along with other specific technical issues. Some practical indications can hence be identified for minimizing the risk of diagnostic errors, encompassing the improvement of diagnostic accuracy by combining clinical evidence with results of chest computed tomography (CT) and RT-PCR, interpretation of RT-PCR results according to epidemiologic, clinical and radiological factors, recollection and testing of upper (or lower) respiratory specimens in patients with negative RT-PCR test results and high suspicion or probability of infection, dissemination of clear instructions for specimen (especially swab) collection, management and storage, together with refinement of molecular target(s) and thorough compliance with analytical procedures, including quality assurance.
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
Xie, X., Zhong, Z., Zhao, W., Zheng, C., Wang, F., & Liu, J. (2020) · Radiology · doi:10.1148/radiol.2020200343 · Cited by 1,960
Shen, Z., Xiao, Y., Kang, L., Ma, W., Shi, L., Zhang, L., Zhou, Z., Yang, J., Zhong, J., Yang, D., et al. (2020) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa203 · Cited by 513
Cited by
COVID-19 diagnosis by routine blood tests using machine learning
Kukar, M., Gunčar, G., Vovko, T., Podnar, S., Černelč, P., Brvar, M., Zalaznik, M., Notar, M., Moškon, S., & Notar, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-90265-9 · Cited by 179
Malaria and COVID-19: Common and Different Findings
Di Gennaro, F., Marotta, C., Locantore, P., Pizzol, D., & Putoto, G. (2020) · Tropical Medicine and Infectious Disease · doi:10.3390/tropicalmed5030141 · Cited by 88
Deep learning with robustness to missing data: A novel approach to the detection of COVID-19
Çallı, E., Murphy, K., Kurstjens, S., Samson, T., Herpers, R., Smits, H., Rutten, M., & van Ginneken, B. (2021) · PLOS ONE · doi:10.1371/journal.pone.0255301 · Cited by 6
Metadata
- Authors
- Lippi, G., Simundic, A.-M., & Plebani, M.
- Venue
- Clinical Chemistry and Laboratory Medicine (CCLM)
- Publisher
- Walter de Gruyter GmbH
- Published
- 2020-03-16
- Volume
- 58
- Issue
- 7
- Pages
- 1070-1076
- ISSN
- 1434-6621 (ISSN-L)
- 1437-4331
- OpenAlex
- W3010719113
- Cited by
- 617
- References
- 2
- FWCI
- 17.50
- 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: Potential preanalytical and analytical vulnerabilities in the laboratory diagnosis of coronavirus disease 2019 (COVID-19). https://app.cochat.ai/papers/10.1515/cclm-2020-0285
- The paper
- Lippi, G., Simundic, A.-M., & Plebani, M. (2020). Potential preanalytical and analytical vulnerabilities in the laboratory diagnosis of coronavirus disease 2019 (COVID-19). Clinical Chemistry and Laboratory Medicine (CCLM). https://doi.org/10.1515/cclm-2020-0285
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.