Baseline exposure–response assessment of metals from dried blood spots and personal household air pollution concentrations among pregnant women in Rwanda: household air pollution intervention network trial
Bibliographic record
Abstract
Exposure to metals from the combustion of biomass via inhalation may result in negative health outcomes affecting a variety of organs and systems. Pregnant women cooking with biomass fuels may be exposed to metals, such as lead (Pb) and cadmium (Cd), and have unique risks for adverse health effects with potential impacts on the growing fetus. We assessed the associations between household air pollution and metals detected in dried blood spots from pregnant women at baseline in Rwanda. We analyzed data from 781 pregnant women aged 18–35 years who resided in rural households using biomass fuel and who were enrolled in the Household Air Pollution Intervention Network (HAPIN ) Trial in Rwanda. We explored associations between 24-h average natural log-transformed personal exposures to fine particulate matter (PM 2.5 ), black carbon (BC), and carbon monoxide (CO) and K + standardized Pb and Cd concentrations using linear regression models adjusted for potential confounders (i.e., age, body mass index, bicycle ownership, fish consumption, and food insecurity). The adjusted models showed positive but non-significant associations between air pollutant concentrations and metals detected in blood: PM₂.₅ and Pb (β coefficient: 0.06; 95% CI − 0.03–0.15), BC and Pb (0.11; 95% CI − 0.01–0.24), PM₂.₅ and Cd (0.04; 95% CI − 0.02–0.09), and BC with Cd (0.02; 95% CI − 0.05–0.09). While associations were not statistically significant, the directionally consistent increases in blood Pb and Cd with increased PM₂.₅ and BC exposures align with existing evidence and underscore the need for continued research and policy action to reduce household air pollution exposures and protect maternal and fetal 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".