Reporting results of human biomonitoring of environmental chemicals to study participants: a comparison of approaches followed in two Canadian studies
Bibliographic record
Abstract
Biomonitoring is used increasingly as an indicator and quantitative measure of exposure; however, there is a large gap in interpreting and communicating biomonitoring results to study participants. Two separate, national biomonitoring initiatives are under way in Canada; the household recruitment-based Canadian Health Measures Survey (CHMS) and the clinic recruitment-based Maternal-Infant Research on Environmental Chemicals (MIREC) Study. The CHMS provides participants with the option to receive all their results, but this option is not provided to MIREC participants. The approach to reporting results to participants depends on the availability of reference ranges and guidelines for which tissue concentrations may be interpreted as being elevated or associated with increased health risks, how participants are recruited, unique vulnerabilities of the population, legislation governing access to personal information, and decisions of research ethics committees. It is the researchers' responsibility to present the best case for their approach and, once the decision has been made, to inform participants about access to their results through the consent process.
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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 | 0.188 | 0.292 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.023 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.002 | 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".