Antioxidants and clinical outcomes of patients with coronavirus disease 2019: A systematic review of observational and interventional studies
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is a newly emerging viral infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Oxidative stress appears to be a prominent contributor to the pathogenicity of SARS-CoV-2. Therefore, we carried out a systematic review of human observational and interventional studies to investigate the role of some antioxidants such as vitamins A, E, D, and C, selenium, zinc, and α-lipoic acid in the main clinical outcomes of subjects with COVID-19. Google Scholar, Cochrane Library, Web of Science, Scopus, and Medline were searched using Medical Subject Headings (MeSH) and non-MeSH terms without restrictions. Finally, 36 studies for vitamins C and D, selenium, and zinc were included in this systematic review; however, no eligible studies were found for vitamins A and E as well as α-lipoic acid. The results showed the promising role of vitamin C in inflammation, Horowitz index, and mortality; vitamin D in disease manifestations and severity, inflammatory markers, lung involvement, ventilation requirement, hospitalization, intensive care unit (ICU) admission, and mortality; selenium in cure rate and mortality; and zinc in ventilation requirement, hospitalization, ICU admission, biomarkers of inflammation and bacterial infection, and disease complications. In conclusion, it seems that antioxidants, especially vitamins C and D, selenium, and zinc, can improve multiple COVID-19 clinical outcomes. Nevertheless, more studies are necessary to affirm these results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".