A harmonized occupational biomonitoring approach
Bibliographic record
Abstract
Biomonitoring has been widely used in assessing exposures in both occupational and public health complementing chemical risk assessments because it measures the concentrations of chemical substances in human body fluids (e.g., urine and blood). Biomonitoring considers all routes and sources of exposure. An occupational biomonitoring guidance document has been elaborated (OECD Occupational Biomonitoring Guidance) within the OECD framework and specifically, the Working Parties on Exposure and Hazard Assessment by scientific experts from 40 institutes and organizations representing 15 countries. The guidance provides practical information for assessing chemical exposures in occupational settings including the three common routes of exposure: inhalation, skin absorption and ingestion due to hand to mouth contact. The elaborated stepwise approach for conducting biomonitoring is tailored for occupational health professionals, scientists, risk assessors, and regulators. It includes methods for selecting appropriate biomarkers, devising sampling strategies, and assessing laboratories for validated analytical methods for the biomarker of interest, and ensuring timely feedback of results. Furthermore, it describes procedures for setting up efficient biomonitoring programs based on the Similar Exposure Group (SEG) approaches. Derived health-based human exposure biomarker assessment values called Occupational Biomonitoring Levels (OBLs) are proposed for use in occupational exposure and risk assessment. It also helps with the interpretation of biomonitoring results routinely collected and procedures for communicating biomonitoring results at individual, collective, and workplace levels. Ethical considerations associated with biomonitoring are also discussed. The ultimate goal of this biomonitoring approach is to promote harmonized application and interpretation of biomarkers as well as evidence-based occupational risk management measures.
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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 | 0.038 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".