Maternal exposure to specific endocrine-disrupting chemicals and gestational diabetes mellitus: systematic review and meta-analysis
Bibliographic record
Abstract
This meta-analysis examines the relationship between exposure to endocrine-disrupting chemicals, such as phthalates and parabens, which are commonly found in cosmetics, and the risk of developing gestational diabetes mellitus (GDM). Following a systematic search of databases (including PubMed, Scopus, and Web of Science), 14 relevant studies involving 9,503 pregnant women from various regions were identified. After excluding one paper, 11 studies were classified as high-quality, while three received acceptable scores and were included in the analysis. The studies assessed the level of chemical exposure by analyzing urine, serum, or plasma samples. Calculated odds ratios (ORs) and 95% confidence intervals (CIs) were used to evaluate the potential association between maternal exposure and GDM development. The pooled analysis indicated no significant correlation between phthalate exposure during pregnancy and GDM risk, with an OR of 1.01 (95% CI: 0.95-1.08). Subgroup analyses based on participants' location and specific phthalate metabolites consistently showed no significant association with GDM. Limited evidence on parabens also failed to demonstrate a clear link with GDM. Although this meta-analysis found no significant link between these substances and GDM, further investigation is necessary to comprehensively assess the cumulative and long-term effects of endocrine-disrupting chemicals on pregnant women.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".