Validation of urinary trichloroacetic acid as a biomarker of exposure to drinking water disinfection by-products
Bibliographic record
Abstract
Disinfection by-products (DBPs) in drinking water represent a public health issue and a challenge for epidemiology to provide evidence towards the causation of various hypothesized health effects. Validation of a biomarker of exposure to DBPs is a strategy to achieve progress which has been advocated. The objective of this study was to validate urinary trichloroacetic acid (TCAA) excretion as a biomarker of exposure to DBPs in an experimental exposure cohort. A total of 52 healthy women participated in the study. Participants consumed supplied tap water for 15 d and provided urine and blood samples for TCAA measurements. The findings revealed that (1) background levels of TCAA in urine and blood were readily detectable, (2) TCAA levels in blood and urine increased with increased amounts of TCAA ingested, (3) the correlations between measurements of TCAA ingestion and urinary excretion were modest (r=0.66, p<0.001) based on one days' sampling and high (r=0.77-0.83, p<0.001) based on two to four days' sampling, (4) the correlations between measurements of TCAA ingestion and blood TCAA concentration were high (r=0.80, p<0.001) and (5) multiple days' urinary TCAA measures improved the prediction of TCAA ingestion through urinary TCAA excretion. TCAA can be a valid biomarker of exposure for DBPs in drinking water.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".