Chemical Mixture Exposures during Pregnancy and Birth Outcomes
Bibliographic record
Abstract
Exposure to environmental chemical mixtures, which is prevalent among pregnant women, may be associated with altered fetal growth and gestational duration. In a prospective cohort of 380 pregnant women from Cincinnati, OH (enrolled 2003-2006), we quantified biomarker concentrations in urine and blood of 35 organic pollutants, cotinine, and 4 metals. We used K-means clustering and non-negative principal component (PC) analysis to characterize chemical mixtures among pregnant women. Then, we used multivariable linear regression to estimate and compare the covariate-adjusted associations of cluster membership or PC scores with gestational-age-specific birth weight z-score, birth length, head circumference, and gestational duration. Geometric mean biomarker concentrations were generally higher among women in cluster 1, intermediate among women in cluster 2, and lowest among women in cluster 3. Chemical biomarkers in the same structural or commercial family loaded onto the same PC. Compared with children born to women in cluster 3, children born to women in clusters 1 and 2 had 0.28 cm (95% CI: -0.86, 0.30) and 0.13 cm (95% CI:-0.65, 0.38) shorter birth length, respectively. Each standard deviation increase in PC 4 (correlated with organochlorine pesticides, cadmium, and lead) and PC 6 (correlated with mercury and monoethyl phthalate (MEP)) was associated with a 0.24 cm (95% CI: -0.49, 0.02) and 0.14 cm (95% CI: -0.42, 0.14) decrease in birth length, respectively. Chemical biomarkers with higher concentrations among women in clusters 1 and 2 loaded more strongly on both PC 4 and PC 6 than other PCs. Neither cluster membership nor PC scores were associated with birth weight z-score, head circumference, or gestational duration. In this cohort, cluster membership and PC scores reflecting exposure to cadmium, mercury, lead, organochlorine pesticides, and MEP were both inversely associated with birth length, but not other fetal growth measures or gestational duration.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".