Variation in urinary spot sample, 24 h samples, and longer-term average urinary concentrations of short-lived environmental chemicals: implications for exposure assessment and reverse dosimetry
Bibliographic record
Abstract
Population biomonitoring data sets such as the Canadian Health Measures Survey (CHMS) and the United States National Health and Nutrition Examination Survey (NHANES) collect and analyze spot urine samples for analysis for biomarkers of exposure to non-persistent chemicals. Estimation of population intakes using such data sets in a risk-assessment context requires consideration of intra- and inter-individual variability to understand the relationship between variation in the biomarker concentrations and variation in the underlying daily and longer-term intakes. Two intensive data sets with a total of 16 individuals with collection and measurement of serial urine voids over multiple days were used to examine these relationships using methyl paraben, triclosan, bisphenol A (BPA), monoethyl phthalate (MEP), and mono-2-ethylhexyl hydroxyl phthalate (MEHHP) as example compounds. Composited 24 h voids were constructed mathematically from the individual collected voids, and concentrations for each 24 h period and average multiday concentrations were calculated for each individual in the data sets. Geometric mean and 95th percentiles were compared to assess the relationship between distributions in spot sample concentrations and the 24 h and multiday collection averages. In these data sets, spot sample concentrations at the 95th percentile were similar to or slightly higher than the 95th percentile of the distribution of all 24 h composite void concentrations, but tended to overestimate the maximum of the multiday concentration averages for most analytes (usually by less than a factor of 2). These observations can assist in the interpretation of population distributions of spot samples for frequently detected analytes with relatively short elimination half-lives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".