The effect of supplementation with vitamins A, B, C, D, and E on disease severity and inflammatory responses in patients with COVID-19: a randomized clinical trial
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Because of the effect of vitamins on modulating the immune system function, we have evaluated the effect of supplementation with vitamins A, B, C, D, and E in ICU-admitted patients with COVID-19. METHODS: This study was a randomized and single-blinded clinical trial in which 60 subjects were randomly assigned to two groups. The intervention group (n=30) received vitamins, and the control group did not receive any vitamin or placebo. The intervention was included 25,000 IU daily of vitamins A, 600,000 IU once during the study of D, 300 IU twice daily of E, 500 mg four times daily of C, and one amp daily of B complex for 7 days. At baseline and after the 7-day intervention, the serum levels of inflammatory markers, vitamins, and the SOFA score were assessed. In addition, the mortality rate and duration of hospitalization were evaluated after the intervention (IRCT registration number: IRCT20200319046819N1/registration date: 2020-04-04, https://www.irct.ir/trial/46838 ). RESULTS: Significant changes were detected in serum levels of vitamins (p < 0.001 for all vitamins), ESR (p < 0.001), CRP (p = 0.001), IL6 (p = 0.003), TNF-a (p = 0.001), and SOFA score (p < 0.001) after intervention compared with the control group. The effect of vitamins on the mortality rate was not statistically significant (p=0.112). The prolonged hospitalization rate to more than 7 days was significantly lower in the intervention group than the control group (p=0.001). Regarding the effect size, there was a significant and inverse association between receiving the intervention and prolonged hospitalization (OR = 0.135, 95% CI 0.038-0.481; p=0.002); however, after adjusting for confounders, it was not significant (OR=0.402, 95% CI 0.086-1.883; p=0.247). CONCLUSION: Supplementation with vitamins A, B, C, D, and E could improve the inflammatory response and decrease the severity of disease in ICU-admitted patients with COVID-19.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".