Regional Priorization of Contaminants of Interest in Environmental Health Based on Human Biomonitoring Data
Bibliographic record
Abstract
BACKGROUND/AIM: Limited financial and human resources command to establish priorities in terms of environmental contaminants of public health interest. The objective of this work was to establish a regional list of priority chemicals based on the interpretation of a relevant subset of Canadian Human biomonitoring (HBM) data.METHODS: HBM data for Quebec’s participants were extracted from the Canadian Health Measures Survey for 50 environmental pollutants. The priority determination was first made by comparing the geometric mean and 95th percentile biomarker concentrations of these data with the baseline levels of the Canadian population. Quebec’s HBM data were then compared to Biomonitoring Equivalent, which are screening tools developed in a health risk assessment context. Using this second approach, chemical-specific hazard quotients (HQs) or cancer risk levels were generated for about ten compounds.RESULTS: The comparison between Quebec and Canadian HBM data allowed to identify 3 chemicals for which Quebec inhabitants biomarkers concentrations are deemed significantly greater than for Canadians in general, based on the non-overlapping of the 95% confidence intervals of the geometric mean. Using the second approach, the level of priority was determined as medium or high for 7 compounds (HQ values >0.1 or cancer risk of >10-6). Due to the limited number of compounds for which Quebec HBM data are currently available, the results of recent prioritization exercises based on Canadian and US HBM data and using BE complemented the present analysis. Overall, 24 substances of interest were identified in the present work. Cadmium, prioritized under the two approaches mentioned above, as well as arsenic, lead and acrylamide figure among those.CONCLUSION: The list of priority chemical built here can contribute to orientate public health actions in order to reduce population’s exposure to critical environmental contaminants, or contribute to identify relevant research themes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".