Comparing United States and Canadian population exposures from National Biomonitoring Surveys: Bisphenol A intake as a case study
Bibliographic record
Abstract
The Centers for Disease Control and Prevention provides biomonitoring data in the United States as part of the National Health and Nutrition Examination Survey (NHANES). Recently, Statistics Canada initiated a similar survey - the Canadian Health Measures Survey (CHMS). Comparison of US and Canadian biomonitoring data can generate hypotheses regarding human exposures from environmental media and consumer products. To ensure that such comparisons are scientifically meaningful, it is essential to first evaluate aspects of the surveys' methods that can impact comparability of data. We examined CHMS and NHANES methodologies, using bisphenol A (BPA) as a case study, to evaluate whether survey differences exist that would hinder our ability to compare chemical concentrations between countries. We explored methods associated with participant selection, urine sampling, and analytical methods. BPA intakes were also estimated to address body weight differences between countries. Differences in survey methods were identified but are unlikely to have substantial impacts on inter-survey comparisons of BPA intakes. BPA intakes for both countries are below health-based guidance values set by the US, Canada and the European Food Safety Authority. We recommend that before comparing biomonitoring data between surveys, a thorough review of methodologic aspects that might impact biomonitoring results be conducted.
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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 | 0.027 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".