Immunological predictors of disease severity in patients with COVID-19 infection
Bibliographic record
Abstract
Background: Identifying immune cells involved in COVID-19 disease progression and predictors of poor outcomes is important to manage patients adequately. Methods: A prospective observational cohort study enrolled 53 mild non-hospitalized and 48 hospitalized confirmed COVID-19 patients to a tertiary hospital in Oman. Results: Hospitalized patients were older (58 years vs 36 years, p <0.001) and had more comorbid conditions like diabetes (65 % Vs 21% p<0.001). Hospitalized patients had significantly higher inflammatory markers (p<0.001); C-reactive protein (CRP) (114 vs 4 mg/L), Interleukin-6 (IL-6) (33 vs 3.71pg/ml), lactate dehydrogenase (LDH) (417 vs 214 U/L), ferritin (760 vs 196 ng/mL), fibrinogen (6 vs 3 g/L), D-dimer (1.0 vs 0.3 mcg/mL), disseminated intravascular coagulopathy (DIC) score (2 vs 0) and neutrophil/lymphocyte ratio (4 vs 1.1), (p<0.001). In multivariate regression analysis, statistically significant independent early predictors of ICU admission or death were higher levels of IL-6 (OR 1.03, p=0.03), frequency of large inflammatory monocytes (CD14+CD16+) (OR 1.117, p=0.010) and frequency of circulating naïve CD4+ T cells (CD27+CD28+CD45RA+CCR7+) (OR 0.476, p=0.03). Conclusion: IL-6, frequency of large inflammatory monocytes, and circulating naïve CD4 T cells can be used as independent immunological predictors of poor outcomes in COVID-19 patients to prioritize critical care and resources.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".