38: Blood Lead, Cadmium and Mercury Levels in Children Receiving Primary Healthcare in Toronto: A Collaborative Study
Bibliographic record
Abstract
There is limited Canadian data on blood lead, cadmium and mercury levels in early childhood, a population that is highly vulnerable to the effects of these metals. Mercury and lead exposures have been linked to neurological effects and cadmium exposures have been linked to kidney and bone damage. Identifying subpopulations with higher levels and the assoicated risk factors is important for directing future clinical and public health intiatives in this age group of Ontario chidren. To build capacity within the public health and primary healthcare sectors, collaboration between Public Health and a primary care research network for children was formed to study blood lead, cadmium and mercury levels in children <6 years of age receiving routine primary healthcare. Children younger than five years of age receiving routine primary healthcare were recruited through this primary-care research network of family physicians, pediatricians, researchers and policy makers. Risk factors for heavy metal exposure were collected through a series of standardized questionnaires administered to the participant's parents. In addition, a small blood sample was obtained from each child and tested for lead, mercury and cadmium. Preliminary heavy metal data was available for 205 children. Mean age was 42 months and 52% of participants were male. 70% of children were Caucasian and 30% were visible minorities. Eighty-five percent of the participants had a household income >$60,000. Geometric mean concentrations of lead, mercury and cadmium were 0.67 μg/dL (95% CI 0.63 μg/dL to 0.73 μg/dL), 0.59 μg/L (95% CI 0.45 μg/L to 0.55 μg/L) and 0.11 μg/L (95% CI 0.11 μg/L to 0.12 μg/L), respectively. Two children had mercury levels above the Health Canada methylmercury guidance of 8 μg/L. No child had abnormal lead or cadmium levels. Through a multidisciplinary collaboration, we have demonstrated that heavy metal exposure can be measured in early childhood during routine primary healthcare. In this preliminary study, two children were identified with elevated mercury blood levels. These findings should be interpreted with caution until a larger, more diverse, population of children is studied.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".