ZINC STATUS IN CHILDREN WITH ALLERGIES: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Allergies pose significant health concerns, particularly in children, where they can adversely affect growth and quality of life. Recent studies have suggested that zinc deficiency may play a critical role in the immune dysregulation associated with allergies. The method used in This systematic review followed the PRISMA guidelines and analyzed studies on the relationship between zinc levels and allergies in children, using data from PubMed, Science Direct, EBSCO, and Cochrane Library, and a meta-analysis was conducted to evaluate the effect size and risk of bias assessed using the Newcastle-Ottawa Scale to examine the relationship between zinc levels and allergies in children. Our findings indicate that children with allergies may exhibit different zinc levels compared to healthy controls, with a pooled effect size of -0.56 (95% CI: [-0.99, -0.13]). The analysis revealed significant heterogeneity (Tau² = 0.39; Chi² = 79.41, df = 8, p < 0.00001; I² = 90%), highlighting the variability across studies and the necessity for further research to standardize the methodologies. These results were statistically significant (Z = 2.54, p = 0.01), suggesting a potential association between zinc levels and allergies in children. Further investigations are needed to explore whether zinc supplementation can support immune function and alleviate allergic symptoms.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.026 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".