Speichel als Matrix für das Humanbiomonitoring in der Arbeits‐ und Umweltmedizin [Biomonitoring Methods in German Language, 2015]
Bibliographic record
Abstract
Saliva as a matrix for human biomonitoring in occupational and environmental medicine Purpose: Human biomonitoring (HBM) implies the assessment of internal exposure to hazardous substances by measuring the substances, their metabolites or reaction products, as well as effect parameters in human body fluids. Along with blood, plasma and urine, saliva is of increasing interest as an alternative matrix for HBM. Methods: This paper reviews studies that measure salivary background levels of hazardous substances, elevated levels after environmental or occupational exposure, as well as references which deal with physiological and toxicokinetic behaviour of saliva and salivary parameters, respectively. Results: The studies revealed that the determination of biomarkers in saliva is a promising approach for HBM, even if only few substances showed a satisfying correlation with exposure data or established biomonitoring matrices such as blood, plasma and urine. Saliva has been proven to be particularly suitable for substances of low molecular weight such as organic solvents, selected pesticides, cotinine, and for some specific trace elements. Besides several advantages, serious problems and limitations were identified. Above all, the complex interactions between substance properties, sampling procedure, sample preparation, measurement techniques or individual factors, and the salivary analyte level are discussed. Conclusions: A major conclusion of the review is that more scientific studies are needed in order to systematically collect data on parameters, influencing salivary analyte levels. Crucially required is a harmonisation of the sampling as well as the sample preparation techniques and procedures, which is indispensable to achieve an overall comparability and interpretability of salivary biomarker levels.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".