An introduction to BASIC Guide: human biomonitoring and surveillance of chemical exposure in occupational settings
Bibliographic record
Abstract
Human biomonitoring (HBM) complements air and surface measurements by integrating exposure from all routes and sources, strengthening occupational exposure assessment and control. In occupational settings, HBM can quantify exposure during routine work and nonroutine activities, evaluate controls, investigate incidents (potential overexposures), and support medical surveillance. To use HBM to its full potential, occupational health and safety professionals (OHPs) should adopt harmonized biomonitoring approaches reflecting best practice. This short communication presents the BASIC Guide series (Human Biomonitoring and Surveillance of Chemical Exposure in Occupational Settings), initiated by the International Society of Exposure Science Human Biomonitoring working group (ISES Europe HBM WG) as an integral part of the HBM Global Network. These chemical-specific practical documents operationalize the OECD (Organisation for Economic Co-operation and Development) occupational biomonitoring guidance, supporting the consistent implementation of exposure biomonitoring programs. Each BASIC Guide provides clear instructions on biomarker selection, sample handling, analytical methods, quality assurance, and result interpretation and communication. By translating international frameworks into actionable protocols, the BASIC Guides improve reproducibility and regulatory alignment in occupational HBM and enable more defensible exposure assessments worldwide.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.047 |
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".