Review of non–invasive biomarkers as a tool for exposure characterization in human health risk assessments
Bibliographic record
Abstract
Blood and urine are historically the most frequent matrices used for measuring chemical levels in human biomonitoring studies. As biomonitoring programs are refreshed, consideration of specific priority substances and specific population targets provide opportunities for inclusion of alternative non- or minimally invasive matrices. This review describes methods used in health risk assessment to characterize exposure and risk based upon biomarkers from noninvasive matrices other than urine or blood, including human milk, hair, fingernails, toenails, exhaled breath, deciduous teeth, sweat, semen, meconium, and feces. Illustrative examples of these methods relevant to chemical management are provided. This review suggests that, although these alternative noninvasive biomarkers are not frequently used in human health risk assessment at present, these biomarkers may prove useful in (1) characterizing exposure and health risk in vulnerable populations, (2) cumulative risk assessments, and (3) community-based risk assessments, depending upon the substance of concern. To incorporate alternative noninvasive biomarkers into human health risk assessments with confidence, more research is needed to improve our knowledge of the relationships between external dose, internal dose, and biologic consequent effects in matrices other than blood and urine.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".