Baseline levels of selected environmental chemicals in Canadians: Results from Canadian Health Measures Survey
Bibliographic record
Abstract
Background: The Canadian Health Measures Survey (CHMS) is an on-going nationally representative direct health measures survey launched in 2007. This survey collects new and important data to fill knowledge gaps on health status of Canadians. The biomonitoring component of CHMS, supported by Canada’s Chemicals Management Plan, collects and analyzes blood and urine samples for several classes of environmental chemicals. Method: Each CHMS cycle spans approximately two years in which about 5500-6500 individuals are selected representing about 96% of the general population in Canada. Respondents aged 6-79 years were selected for CHMS (2007-2009) while the subsequent CHMS cycles (CHMS 2009-2011; CHMS 2012-2013) include 3-5 years olds. The respondents completed a household interview and visited a mobile examination centre where the physical health measurements were performed and blood and urine samples were collected. Biomarkers for about 90 environmental chemicals were analyzed in the respondent’s bio-specimens. Results: Descriptive statistics (arithmetic and geometric means, and selected percentiles) were computed on the levels of individual biomarkers in Canadians. Baseline levels of selected group of environmental chemicals (including heavy metals, pesticides and emerging contaminants) from CHMS 2007-2009 and 2009-2011 (to be released in April 2013) will be presented. These biomarker levels in Canadians will be compared with similar findings reported in other population surveys. Preliminary analysis on the temporal trends in the biomarker levels will be highlighted. Conclusion: Nationally representative data on the levels of biomarkers from CHMS provides valuable information for current and future human health risk assessments and for assessing potential links between chemical exposures and health outcomes. Moreover, it allows comparison of similar data from other countries and helps in monitoring temporal and geospatial trends in biomarker levels.
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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 | 0.001 | 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) | 0.006 | 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 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".