Opportunities and Challenges From Leading Trends in a Biomonitoring Project: Canadian Health Measures Survey 2007–2017
Bibliographic record
Abstract
Background Biomonitoring can be conducted via the assessment of the levels of chemicals in human bodies and their surroundings, for example, as in the Canadian Health Measures Survey (CHMS). This study aims to report the leading increasing or decreasing biomarker trends and determine their significance. Methods We implemented a trend analysis for all variables from the CHMS biomonitoring data cycles 1 to 5 conducted between 2007 and 2017. The associations with time and obesity were determined with linear regressions using the CHMS cycles and body mass index (BMI) as predictors. Results There were 997 unique biomarkers identified and 86 biomarkers with significant trends across cycles. Nine of the ten leading biomarkers with the largest decreases were environmental chemicals, and the levels of 1,2,3-trimethylbenzene, dodecane, palmitoleic acid, and o-xylene decreased by more than 60%. All of the ten chemicals with the largest increases were environmental chemicals, and the levels of 1,2,4-trimethylbenzene, nonanal, and 4-methyl-2-pentanone increased by more than 200%. None of the twenty biomarkers with the largest increases or decreases between cycles were associated with BMI. Conclusions Opportunities in the CHMS include the feasibility of determining the associations between biomarkers and time or BMI. The challenges include the unknown causes of trends with large magnitudes of increase or decrease and their unclear impact on Canadians’ health. We recommend that the CHMS to plan future cycles with reference to the leading trends and to measure chemicals with both human and environmental samples.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".