Postnatal Growth Following Prenatal Lead Exposure and Calcium Intake
Bibliographic record
Abstract
BACKGROUND: The effects on postnatal growth of maternal exposure to low levels of lead during pregnancy have not been well established. In addition, information is limited regarding the protective effect of dietary calcium intake during pregnancy against the effect of lead for fetal and postnatal growth. We investigated the relationship between prenatal exposure to lead and growth at birth and 6, 12, and 24 months postnatal, and evaluated the role of calcium intake against the effect of lead. METHODS: A total of 1150 pregnant women, and their subsequent offspring, enrolled in a prospective birth cohort study (Mothers and Children's Environmental Health Study), were evaluated. Multivariable regression analysis was conducted to estimate the effects of prenatal maternal blood lead levels on growth at each follow-up. RESULTS: The blood lead levels of participating mothers were <5.0 μg/dL and mean levels were 1.25 μg/dL during the early (before 20 gestational weeks) and late (at delivery) gestational periods. Prenatal exposure to lead, particularly in late pregnancy, was significantly associated with a reduction in infantile growth at 24 months. When pregnant women had dietary calcium intake at mean or upper level, the association was not significant. In contrast, lower than mean level of calcium intake intensified the adverse effect of prenatal lead exposure on growth in children. CONCLUSIONS: Prenatal lead exposure <5.0 μg/dL adversely affects postnatal growth and low calcium intake aggravates the effect, indicating more stringent control of lead and sufficient intake of calcium are necessary to help children's health.
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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".