The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Lymphocyte Subset Counts in COVID‐19 Patients: A Meta‐Analysis
Open accessAbstract
A reduced peripheral blood absolute lymphocyte count with an elevated neutrophil count has been a consistent observation in hospitalized coronavirus disease 2019 (COVID‐19) patients. In this brief meta‐analysis, the reduction of lymphocyte subset counts in COVID‐19 patients was investigated across 20 peer‐reviewed studies meeting criteria for reporting lymphocyte subset counts and COVID‐19 disease severity. CD4+ T cell, CD8+ T cell, B cell, NK cell, and total lymphocyte cell counts all showed statistically significant reduction in patients with severe/critical COVID‐19 disease compared to mild/moderate disease. T‐cell subsets showed the largest standardized magnitude of change. In some studies, multivariate analysis has shown that CD4 and/or CD8 T‐cells counts are independently predictive of patient outcomes. © 2020 International Society for Advancement of Cytometry
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
Conducting Meta-Analyses in R with the metafor Package
Viechtbauer, W. (2010) · Journal of Statistical Software · doi:10.18637/jss.v036.i03 · Cited by 20,464
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
Wan, X., Wang, W., Liu, J., & Tong, T. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-135 · Cited by 11,735
Reduction and Functional Exhaustion of T Cells in Patients With Coronavirus Disease 2019 (COVID-19)
Diao, B., Wang, C., Tan, Y., Chen, X., Liu, Y., Ning, L., Chen, L., Li, M., Liu, Y., Wang, G., et al. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.00827 · Cited by 2,475
Pathological inflammation in patients with COVID-19: a key role for monocytes and macrophages
Merad, M., & Martin, J. C. (2020) · Nature Reviews Immunology · doi:10.1038/s41577-020-0331-4 · Cited by 2,472
Henry, B. M., de Oliveira, M. H. S., Benoit, S., Plebani, M., & Lippi, G. (2020) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2020-0369 · Cited by 1,964
Du, R.-H., Liang, L.-R., Yang, C.-Q., Wang, W., Cao, T.-Z., Li, M., Guo, G.-Y., Du, J., Zheng, C.-L., Zhu, Q., et al. (2020) · European Respiratory Journal · doi:10.1183/13993003.00524-2020 · Cited by 1,452
Lymphopenia in severe coronavirus disease-2019 (COVID-19): systematic review and meta-analysis
Huang, I., & Pranata, R. (2020) · Journal of Intensive Care · doi:10.1186/s40560-020-00453-4 · Cited by 662
Cited by
Predictors of COVID‐19 severity: A literature review
Gallo Marin, B., Aghagoli, G., Lavine, K., Yang, L., Siff, E. J., Chiang, S. S., Salazar‐Mather, T. P., Dumenco, L., Savaria, M. C., Aung, S. N., et al. (2021) · Reviews in Medical Virology · doi:10.1002/rmv.2146 · Cited by 819
Chattree, V., Singh, K., Singh, K., Goel, A., Maity, A., & Lone, A. (2022) · Journal of Food Biochemistry · doi:10.1111/jfbc.14259 · Cited by 19
Metadata
- Authors
- Huang, W., Berube, J., McNamara, M., Saksena, S., Hartman, M., Arshad, T., Bornheimer, S. J., & O'Gorman, M.
- Venue
- Cytometry Part A
- Publisher
- Wiley
- Published
- 2020-07-18
- Volume
- 97
- Issue
- 8
- Pages
- 772-776
- ISSN
- 1552-4922 (ISSN-L)
- 1552-4930
- OpenAlex
- W3034366532
- Cited by
- 237
- References
- 8
- FWCI
- 5.76
- 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: Lymphocyte Subset Counts in COVID‐19 Patients: A Meta‐Analysis. https://app.cochat.ai/papers/10.1002/cyto.a.24172
- The paper
- Huang, W., Berube, J., McNamara, M., Saksena, S., Hartman, M., Arshad, T., Bornheimer, S. J., & O'Gorman, M. (2020). Lymphocyte Subset Counts in COVID‐19 Patients: A Meta‐Analysis. Cytometry Part A. https://doi.org/10.1002/cyto.a.24172
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.