The Role of Minerals in COVID-19: An Umbrella Review
Bibliographic record
Abstract
Background: This umbrella review aims to synthesize the existing literature on the preventive and therapeutic benefits of minerals zinc, selenium, iron, copper, magnesium, phosphorus, and calcium in the context of COVID-19 prevention and management. The objective is to highlight the clinical applicability and identify avenues of future research. Methods: A systematic search was conducted in PubMed and Google Scholar databases using predefined keywords for each mineral combined with COVID-19–related terms. Narrative and systematic reviews were included, following Cochrane guidelines. AMSTAR scoring was used to assess systematic review quality, while SANRA guidelines were used to evaluate narrative reviews. Data extraction and synthesis were performed, and reference overlap analysis was conducted (see Table S1 in the supplemental material). Results: Narrative reviews highlighted the range of therapeutic properties of minerals including antimicrobial, antiviral, antioxidant, anti-inflammatory, and immune-modulating and the essential role they play in the prevention and treatment of many conditions, including acute respiratory conditions such as COVID-19. The systematic reviews highlighted that deficiency of key minerals such as zinc, selenium, iron, copper, magnesium, phosphorus, and calcium are associated with increased risk of infection and decreased rate of recovery. Iron supplementation may be beneficial as functional anemia is common in those with COVID-19. Zinc supplementation may shorten the duration of olfactory dysfunction. Conclusion/Summary: Deficiency of minerals may increase the risk of infection and decrease the rate of recovery as it relates to COVID-19. Supplementation with and correction of zinc, iron and selenium deficiencies may improve clinical outcomes and immune responses in those with COVID-19."
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".