Maternal Blood Manganese Levels and Infant Birth Weight
Bibliographic record
Abstract
BACKGROUND: Manganese is both an essential element and a known neurotoxicant to children. High manganese exposures have been associated with negative reproductive outcomes in animals, but few epidemiologic studies have examined the effects of human fetal manganese exposure. METHODS: We studied the association between maternal and umbilical cord blood manganese levels and birth weight in a cohort of 470 mother-infant pairs born at term (>or=37 weeks gestation) in Ottawa County, Oklahoma. Nonlinear spline and quadratic regression models were used to test the hypothesis of an inverted U-shaped relationship between manganese levels and birth weight. RESULTS: Mean (standard deviation) concentration of manganese was 2.4 (0.95) microg/dL in the maternal blood and 4.2 (1.6) microg/dL in the cord blood. Umbilical cord manganese was not associated with birth weight. A nonlinear relationship was observed between maternal manganese and birth weight after adjusting for potential confounders. Birth weight increased with manganese levels up to 3.1 microg/L, and then a slight reduction in weight was observed at higher levels. Compared with the 3.1-microg/L point of inflection, birth weight estimates at the 5th (1.3 microg/L) and 95th (4.0 microg/L) percentiles of exposure were -160 g (95% confidence interval = -286 to -33) and -46 g (-38 to 131), respectively. CONCLUSIONS: Maternal blood manganese levels during pregnancy are associated with birth weight in a nonlinear pattern in full-term infants. These findings suggest that manganese may affect fetal growth. Possible detrimental effects of elevated manganese levels on the fetus should be further examined in more highly exposed populations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".