Association between Maternal Selenium Exposure and Congenital Heart Defects in Offspring: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: The association between congenital heart defects (CHDs) and selenium (Se) is still unclear. We aimed to systematically review and quantitative analyze the potential relationship between maternal Se exposure and CHDs in the offspring. Methods: PubMed, Embase, Web of Science and Scopus databases were searched from inception up to August 2021 for relevant studies. Methodological quality of the studies was assessed through Newcastle-Ottawa scale. The Standard mean difference (SMD) and corresponding 95% confidence interval (CI) were calculated to compare maternal Se levels between CHDs groups and control groups using a random-effects model. Results: Four articles covering five studies were included in the systematic review, and three articles covering four studies were included in the meta-analysis. One study measured Se concentrations in maternal hair and found a positive correlation between high concentrations and increased risk of CHDs in offspring. However, one study on cord blood, and one on whole blood illustrated that Se exposure was associated with decreased risk of CHDs. There was no significant association found between serum Se levels and CHDs in two studies. Pooled results showed decreased Se levels in the circulation of mothers with CHDs offspring (SMD = -108.27, 95% CI: -192.72, -23.82), with statistically significant heterogeneity (I2 = 99.8%, P < 0.001) but not in hair, as compared with controls. Conclusion: Low maternal Se status may be associated with increased risk of CHDs in offspring. However, further larger-scale studies with strict and consistent design methods are still required to investigate this issue.
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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) | 0.017 | 0.031 |
| 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".