Association between serum copper and childhood asthma: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Asthma is a chronic respiratory disease with complex pathogenesis. Some studies suggest that certain trace metals may be associated with asthma. However, the relationship between serum copper (Cu) and childhood asthma remains unclear. This meta-analysis evaluates the association between Cu and childhood asthma. Methods: Studies of multiple databases were searched from inception to 2024. We recorded the standardized mean difference (SMD), 95% confidence intervals (CIs), and other data. The analysis was performed using Stata 18.0 software. Two independent reviewers appraised methodological quality using the Newcastle–Ottawa Scale. Sensitivity analysis was used to test robustness. To evaluate publication bias, we used Begg’s funnel plots and Egger’s regression test. Results: A total of 11 studies with a combined 1006 participants were included. There was no significant difference in the level of serum Cu between children with asthma cases and controls (SMD = −0.032, 95% CI: −0.291–0.228, P = 0.811). There was significant heterogeneity among the studies (I2 = 73.5%, P < 0.0001). Subgroup analysis demonstrated that heterogeneity was not caused by the continent of origin, publication year, sample size, detection methods, and the mean age of participants. No publication bias was found. Conclusion: There is no statistically significant association between serum Cu levels and childhood asthma. Further research, particularly large-scale prospective cohort studies, is needed to clarify this relationship.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".