A review of Health Canada’s progress on human biomonitoring-based risk assessments and the path forward
Bibliographic record
Abstract
Since the launch of the Chemicals Management Plan (CMP) in 2006, Health Canada has initiated screening-level risk assessments (RAs) of approximately 4300 priority substances under the Canadian Environmental Protection Act, 1999 (CEPA). With the availability of nationally representative human biomonitoring (HBM) data, over 300 of these substances were assessed using HBM-based RA approaches. Qualitative and quantitative HBM-based RA approaches for the regulatory risk assessment of the general population of Canada were developed to increase the efficiency of screening the potential health risk of CMP priority substances. To support HBM-based RAs, several biomonitoring equivalents (BE) were derived to interpret HBM data. For some CMP substances, Health Canada conducted cumulative risk assessments of chemical mixtures using HBM data as measures of exposure. In 2023, CEPA was amended to include the assessment of populations who may be disproportionately impacted (vulnerable populations) and the cumulative effects of multiple chemicals. Going forward, Health Canada is exploring modern approaches in HBM-based RAs, including biomarkers of effect and non-traditional biomarkers (e.g., hair, nails) to address CEPA amendments. This manuscript will discuss Health Canada's progress in HBM-based RAs, and the possible path forward in using HBM data to strengthen human health risk assessments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".