Micronutrient perspective on COVID-19: Umbrella review and reanalysis of meta-analyses
Bibliographic record
Abstract
Micronutrients are clinically important in managing COVID-19, and numerous studies have been conducted, but inconsistent findings exist. To explore the association between micronutrients and COVID-19. PubMed, Web of Science, Embase, Cochrane Library and Scopus for study search on July 30, 2022 and October 15, 2022. Literature selection, data extraction and quality assessment were performed in a double-blinded, group discussion format. Meta-analysis with overlapping associations were reconsolidated using random effects models, and narrative evidence was performed in tabular presentations. 57 reviews and 57 latest original studies were included. 21 reviews and 53 original studies were of moderate to high quality. Vitamin D, vitamin B, zinc, selenium, and ferritin levels differed between patients and healthy people. Vitamin D and zinc deficiencies increased COVID-19 infection by 0.97-fold/0.39-fold and 1.53-fold. Vitamin D deficiency increased severity 0.86-fold, while low vitamin B and selenium levels reduced severity. Vitamin D and calcium deficiencies increased ICU admission by 1.09 and 4.09-fold. Vitamin D deficiency increased mechanical ventilation by 0.4-fold. Vitamin D, zinc, and calcium deficiencies increased COVID-19 mortality by 0.53-fold, 0.46-fold, and 5.99-fold, respectively. The associations between vitamin D, zinc, and calcium deficiencies and adverse evolution of COVID-19 were positive, while the association between vitamin C and COVID-19 was insignificant. <b>REGISTRATION:</b> PROSPERO CRD42022353953.
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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 | 0.000 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.104 | 0.005 |
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; both teacher heads agree on what is shown here.
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".