Evaluation of Plasma Lead Levels in Pregnancy and Outcome Implications, Kinshasa, DR Congo
Bibliographic record
Abstract
The aim of this work was to evaluate plasma Pb levels in pregnancy and their birth outcomes implications.For analysis (n = 396 pregnant women with 56 fetal-maternal clusters), plasma samples were diluted quantitatively with a matrix modifier solution and Pb levels were measured using an atomic absorption spectrophotometer (AA500FG).Compared to women with a normal Body Mass Index, underweight, overweight and obese women group had increased levels of plasma Pb (t-test, p=0.0395).Levels of plasma Pb were also observed in women with a family history of preeclampsia and diabetes mellitus (t-test, p=0.0050 and 0.0312, respectively).At delivery, plasma Pb levels were significantly higher in women as compared to prenatal period [means (±SD), 3.387 µg/L (± 0.965) in 37-42 weeks, 2.060 µg/L (± 0.980) in 20-36 weeks and 1.543 µg/L (± 0.709) in 10-19 weeks, ANOVA, p < 0.0001] and newborns showed higher plasma Pb levels than their mothers [means (±SD), 2.304 µg/L (± 0.644) versus 2.067 µg/L (± 1.067), t-test, p < 0.0001].Globally, plasma Pb levels show no significant linear negative correlation to all of the birth outcomes (weight, height, ponderal index, Apgar score, gestational age, head circumference).
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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 | 0.001 |
| 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 | 0.000 | 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".