Lead Exposure in Infancy and Subsequent Childhood Growth
Bibliographic record
Abstract
Studies suggest that elevated postnatal blood lead levels (BLLs) are negatively associated with child growth. This study aimed to investigate associations of childhood BLLs at age one-year and growth outcomes at age six-years (n=661) in a cohort of children in Allada, Benin. The growth outcomes studied are weight-for-age Z-score (WAZ), height-for-age Z-score (HAZ) and BMI-for-age Z-score (BMIZ), head circumference (HC), underweight, stunting, and wasting. Multivariable regression models examined associations between blood lead levels, growth outcomes with adjustment for potential confounders. The geometric mean BLLs was 59.3 μg/L and 82% of children had BLLs >35 μg/L at one year. After adjusting for confounding factors, no association was found between BLLs quartiles and HAZ, WAZ, BMIZ, and height and weight growth velocities. However, boys in the highest quartile had 1.02 cm lower HC (95% CI: [-1.81, -0. 24]) as compared to those in the lowest quartile with a dose-response trend across quartiles (Ptrend =0.02). Furthermore, an increased risk of being stunted was observed in children in the highest quartile of exposure compared to the first (OR: 2.43; 95% CI: [1.11 – 5.33]) with a dose-response trend (Ptrend =0.03). Blood lead was found to be associated with an increased risk of childhood stunting and lower head circumference in a resource-limited setting.
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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 | 0.000 | 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".