Lymphocyte and Platelet Counts, as well as Interleukin-6 Levels, Predict Mortality of Coronavirus Disease 2019 (COVID-19): A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective. To systematically evaluate the value of lymphocytes, platelets, and interleukin-6 in predicting the mortality of patients with coronavirus disease 2019 (COVID-19) and to provide medical evidence for the long-term prognosis of patients with COVID-19. Methods. The latest studies published until July 1, 2021, were retrieved from databases including PubMed, Embase, and Cochrane Library to analyze the ability of lymphocyte and platelet counts as well as interleukin-6 levels to predict mortality in patients with COVID-19. Two reviewers independently screened the literature and extracted data, then evaluated the risk of bias of included studies using the Newcastle–Ottawa Scale (NOS), and used Stata 15.0 software for meta-analysis. Results. A total of nine studies were included, involving 4340 patients. There were 1330 patients in the death group and 3010 patients in the survival group. Meta-analysis showed that, compared with the survival group, lymphocyte counts in the death group were significantly lower (SMD = −0.64, 95% CI: −0.86–−0.43, p < 0.01 ), platelet counts were significantly lower (SMD = −0.47, 95% CI: −0.67–−0.27, p < 0.01 ), and interleukin-6 levels were significantly higher (SMD = 1.07, 95% CI: 0.62–1.53, p < 0.01 ). Conclusion. Lymphocyte and platelet counts, as well as interleukin-6 levels, can help predict the mortality of patients with COVID-19. Due to the limitation of the number and quality of the included studies, these conclusions need to be validated by additional high-quality studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".