Human biomonitoring reference values for metals and trace elements in blood and urine derived from the Canadian Health Measures Survey 2007–2013
Bibliographic record
Abstract
Human biomonitoring reference values are statistical estimates that indicate the upper margin of background exposure to a given chemical at a given time. Nationally representative human biomonitoring data on 176 chemicals, including several metals and trace elements, are available in Canada from 2007 to 2013 through the Canadian Health Measures Survey (CHMS). In this work, we used a systematic approach based on the reference interval concept proposed by the International Federation of Clinical Chemistry and Laboratory Medicine and the International Union of Pure and Applied Chemistry to derive reference values (RV 95 s) for metals and trace elements. These RV 95 s were derived for blood and urine matrices in the general Canadian population based on the latest biomonitoring data from the CHMS. Biomarkers were chosen based on specific selection criteria, including widespread detection in Canadians (≥66% detection rate). Reference populations were created for each biomarker by applying appropriate exclusion criteria. Age and sex were evaluated as possible partitioning criteria and separate RV 95 s were derived for the sub-populations in cases where partitioning was deemed necessary. The RV 95 s for metals and trace elements in blood ranged from 0.18 μg/L for cadmium in young children aged 3–5 years to 7900 μg/L for zinc in males aged 20–79 years. In the case of urinary biomarkers, the RV 95 s ranged from 0.17 μg/L for antimony in the total population aged 3–79 years to 1400 mg/L for fluoride in adults aged 20–79 years. These RV 95 s represent the first set of reference values for metals and trace elements in the general Canadian population. We compare the RV 95 s from other countries where available and discuss factors that could influence such comparisons.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".