Arsenic levels among pregnant women and newborns in Canada: Results from the Maternal-Infant Research on Environmental Chemicals (MIREC) cohort
Bibliographic record
Abstract
Arsenic is a common environmental contaminant from both naturally-occurring and anthropomorphic sources and human exposure can be detected in various tissues. Its toxicity depends on many factors including the chemical form, valence state, bioavailability, metabolism and detoxification within the human body. Of paramount concern, particularly with respect to health effects in children, is the timing of exposure as the prenatal and early life periods are more susceptible to toxic effects. The Maternal-Infant Research on Environmental Chemicals (MIREC) cohort was established to obtain national-level biomonitoring data for approximately 2,000 pregnant women and their infants between 2008 and 2011 from 10 Canadian cities. We measured total arsenic (As) in 1st and 3rd trimester maternal blood, umbilical cord blood, and infant meconium and speciated arsenic in 1st trimester maternal urine. Most pregnant women had detectable levels of total arsenic in blood (92.5% and 87.3%, respectively, for 1st and 3rd trimester); median difference between 1st and 3rd trimester was 0.1124µg/L (p<0.0001), but paired samples were moderately correlated (Spearman r=0.41, p<0.0001). Most samples were below the LOD for umbilical cord blood (50.9%) and meconium (93.9%). In 1st trimester urine samples, a high percentage (>50%) of arsenic species (arsenous acid (As-III), arsenic acid (As-V), monomethylarsonic acid (MMA), and arsenobetaine (AsB)) were also below the limit of detection, except dimethylarsinic acid (DMA). DMA (>85% detected) ranged from <LOD to 64.42 (95th percentile: 11.99)µgAs/L. There was a weak but significant correlation between total arsenic in blood and specific gravity-adjusted DMA in urine (Spearman r=0.33, p<0.0001). Among this population of pregnant woman and newborns, levels of arsenic measured in blood and urine were lower than national population figures for Canadian women of reproductive age (20-39 years). In general, higher arsenic levels were observed in women who were older, foreign-born (predominantly from Asian countries), and had higher education. Further research is needed to elucidate sources of exposure and factors that may influence arsenic exposure in pregnant women and children.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".