Nutrient supplementation for prevention of viral respiratory tract infections in healthy subjects: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract It remains uncertain as to whether nutrient supplementation for the general population considered healthy could be useful in the prevention of RTIs, such as COVID‐19. In this systematic review and meta‐analysis, the evidence was evaluated for primary prevention of any viral respiratory tract infection (RTI) such as SARS‐CoV‐2, through supplementation of nutrients with a recognized role in immune function: multiple micronutrients, vitamin A, folic acid, vitamin B12, C, D, E, beta‐carotene, zinc, iron and long‐chain polyunsaturated fatty acids. The search produced 15,163 records of which 93 papers (based on 115 studies) met the inclusion criteria, resulting in 199,055 subjects (191,636 children and 7,419 adults) from 37 countries. Sixty‐three studies were included in the meta‐analyses, which was performed for children and adults separately. By stratifying the meta‐analysis by world regions, only studies performed in Asia showed a significant but heterogeneous protective effect of zinc supplementation on RTIs (RR 0.86, 95% CI 0.7–0.96, I2 = 79.1%, p = .000). Vitamin D supplementation in adults significantly decreased the incidence of RTI (RR 0.89, 95% CI 0.79–0.99, p = .272), particularly in North America (RR 0.82 95% CI 0.68–0.97), but not in Europe or Oceania. Supplementation of nutrients in the general population has either no or at most a very limited effect on prevention of RTIs. Zinc supplementation appears protective for children in Asia, whilst vitamin D may protect adults in the USA and Canada. In 10/115 (8.7%) studies post‐hoc analyses based on stratification for nutritional status was performed. In only one study zinc supplementation was found to be more effective in children with low zinc serum as compared to children with normal zinc serum levels.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".