Concentrations versus amounts of biomarkers in urine: a comparison of approaches to assess pyrethroid exposure
Bibliographic record
Abstract
BACKGROUND: Assessment of human exposure to non-persistent pesticides such as pyrethroids is often based on urinary biomarker measurements. Urinary metabolite levels of these pesticides are usually reported in volume-weighted concentrations or creatinine-adjusted concentrations measured in spot urine samples. It is known that these units are subject to intra- and inter-individual variations. This research aimed at studying the impact of these variations on the assessment of pyrethroid absorbed doses at individual and population levels. METHODS: Using data obtained from various adult and infantile populations, the intra and inter-individual variability in the urinary flow rate and creatinine excretion rate was first estimated. Individual absorbed doses were then calculated using volume-weighted or creatinine-adjusted concentrations according to published approaches and compared to those estimated from the amounts of biomarkers excreted in 15- or 24-h urine collections, the latter serving as a benchmark unit. The effect of the units of measurements (volume-weighted or creatinine adjusted concentrations or 24-h amounts) on results of the comparison of pyrethroid biomarker levels between two populations was also evaluated. RESULTS: Estimation of daily absorbed doses of permethrin from volume-weighted or creatinine-adjusted concentrations of biomarkers was found to potentially lead to substantial under or overestimation when compared to doses reconstructed directly from amounts excreted in urine during a given period of time (-70 to +573% and -83 to +167%, respectively). It was also shown that the variability in creatinine excretion rate and urinary flow rate may introduce a bias in the case of between population comparisons. CONCLUSION: The unit chosen to express biomonitoring data may influence the validity of estimated individual absorbed dose as well as the outcome of between population comparisons.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".