Cytokine, chemokine, and acute-phase protein profiles in plasma as correlative biomarkers of clinical outcomes for patients with COVID-19
Bibliographic record
Abstract
Coronavirus disease identified in 2019 (COVID-19), caused by severe acute respiratory syndrome-coronavirus-2, had a global impact on human health and the economy. The aim of this study was to quantify cytokines, chemokines, and acute phase proteins in the plasma of patients with COVID-19 to elucidate potential biomarkers to inform prognostic and treatment decisions. Clustering analysis using the K-prototypes method identified underlying biological patterns in patients with COVID-19. The penalized multinomial logistic regression analysis identified two comorbidities (hypertension, congestive heart failure) and thirteen analytes as potential risk factors for COVID-19 progression with 88.2% accuracy. Based on a patient's age, high concentrations of interleukin (IL)-6, monocyte chemoattractant protein-1, and pentraxin 3 were important biomarkers for lethal COVID-19. Decreased concentrations of interferon gamma-induced protein-10, IL-10, and soluble tumor necrosis factor receptor I were found to be associated with mild COVID-19, while increasing concentrations of these analytes could be used to predict COVID-19 severity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".