Human biomonitoring reference values for some non-persistent chemicals in blood and urine derived from the Canadian Health Measures Survey 2009–2013
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Canadian Health Measures Survey collects nationally representative human biomonitoring data on a suite of chemicals and their metabolites, including many non-persistent chemicals. Data has been collected on non-persistent chemicals, including acrylamide, chlorophenols, environmental phenols and triclocarban, organophosphate insecticides, phthalates, polycyclic aromatic hydrocarbon, pyrethroid insecticides, and volatile organic compounds from 2009 to 2013. Using a systematic approach building 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, we derive human biomonitoring reference values (RV95s) for these classes of non-persistent chemicals in blood and urine for the general Canadian population. RV95s were derived for biomarkers of non-persistent chemicals with widespread detection in Canadians (>66% detection rate). Samples with urinary creatinine levels outside the recommended range of 0.3–3.0 μg/L were excluded. Reference populations were constructed by applying smoking and fasting as exclusion criteria where appropriate. Age and sex were evaluated as possible partitioning criteria and separate RV95s were derived for sub-populations in cases where partitioning was deemed necessary. Reference values were derived for 40 biomarkers and represent the first set of RV95s for non-persistent chemicals in the general Canadian population. These values provide a measure of the upper margin of background exposure in the general population and can be compared against individual and population human biomonitoring data. RV95s can be used to by public health officials to identify individuals with high exposures, and by risk assessors and risk managers to identify atypical exposures or subpopulations with elevated exposures.
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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 | 0.001 | 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) | 0.000 | 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 it