Meta-analysis of maternal exposure to heavy metals (lead, cadmium, mercury, chromium) and adverse pregnancy outcomes
Bibliographic record
Abstract
BACKGROUND: The association of heavy metal exposure with adverse pregnancy outcomes (APOs) remains a subject of debate. The goal of the present study was to analyze the correlation of heavy metals (lead [Pb], cadmium [Cd], mercury [Hg], chromium [Cr]) with APOs. METHODS: A systematic review was carried out across four databases: The Cochrane Library, PubMed, Web of Science, and Embase up to November 19, 2024. Two independent researchers performed the literature screening and data extraction based on predefined eligibility criteria. Then, based on the selected literature, the researchers extracted the odds ratios (ORs) and confidence intervals (CIs) of heavy metals (Pb, Cd, Hg, and Cr) and pregnancy outcomes (preterm birth, low birth weight [LBW], and small gestational age [SGA]). The Newcastle-Ottawa Scale (NOS) was adopted for quality assessment. Heterogeneity tests, sensitivity checks, subgroup evaluations, and publication bias analyses were implemented via Stata 15.1 software. RESULTS: This meta-analysis incorporated 24 studies, involving 52,390 subjects. The results demonstrated a significant association of Pb concentration with preterm birth (OR = 1.4, 95% CI: 1.17, 1.68; P < 0.001) and SGA (OR = 1.66, 95% CI: 1.04, 2.66; P = 0.034), though not with LBW. Cd concentration was associated with LBW (OR = 1.10, 95% CI: 1.00, 1.20; P = 0.041) and SGA (OR = 1.27, 95% CI: 1.08, 1.48; P = 0.003), but not with preterm birth. No significant association was observed between Hg concentration and preterm birth. Due to insufficient data, the impact of Cr was not analyzed. Subgroup analysis suggested that region, study design, measurement time, and measurement source may influence the conclusions. CONCLUSIONS: This meta-analysis indicated that Pb exposure was associated with elevated risks of both preterm birth and SGA. Cd exposure exhibited significant associations with LBW and SGA. However, the limited number of articles for other indicators warrants further investigation. Future prospective, high-quality studies are encouraged to explore these relationships further.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.045 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".