Mortality and Clinical Outcomes of Hospitalized COVID-19 Patients is Associated with Serum Concentrations of Selenium and Vitamin D
Bibliographic record
Abstract
Background: Coronavirus disease (COVID-19) has imposed serious effects on public health.The main defender against this viral inflammatory disease is the body's immune system.Selenium and vitamin D as anti-inflammatory, immune-enhancing micronutrients could be beneficial in alleviating the worst outcomes of COVID-19.Methods: One hundred hospitalized COVID-19 patients with saturation of oxygen (SpO2) < 94 were assessed.In the first day of admission to the hospital, serum selenium and 25-hydroxy vitamin D concentrations were measured.Other clinical outcomes, including lung involvement, length of hospital stay (LOS), C-reactive protein (CRP), SpO2, intubation, and gastrointestinal and neural symptoms were extracted from each patient's medical record.Twenty-four-hour food recall was taken to evaluate the food intake of patients.Results: Fifty-six percent of patients were 25-hydroxy vitamin D deficient, and 2 percent were selenium deficient.After adjusting for confounding variables, serum selenium was negatively associated with mortality (coefficient: -0.16, p-value: 0.01) and both selenium (coefficient: -0.10, p-value: 0.01) and 25-hydroxy vitamin D (coefficient: -0.11, p-value: 0.004) showed inverse correlation with LOS. Conclusion:There is an inverse association between serum concentrations of selenium and 25-hydroxy vitamin D with adverse clinical outcomes and mortality of patients with severe COVID-19.Higher concentrations of selenium were associated with increased SpO2 and decreased LOS and risk of death.Although higher concentrations of 25-hydroxy vitamin D were associated with reduced LOS and percentage of lung involvement, no association was found regarding mortality.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".