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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".