Reliability of using urinary and blood trichloroacetic acid as a biomarker of exposure to chlorinated drinking water disinfection byproducts
Bibliographic record
Abstract
This study was designed to analyse the reliability of using urinary and blood trichloroacetic acid (TCAA) as a biomarker of exposure. A total of 46 healthy women consumed supplied TCAA-containing tap water for 15 days and provided urine and blood samples for TCAA measurements. The findings revealed that the reliability of measurements was excellent by using measures of TCAA ingestion, blood concentration and urinary excretion (intraclass correlation coefficients (ICC) > 0.75, p < 0.001). Volume of tap water consumption (ICC = 0.69) and creatinine-adjusted urinary concentration (ICC = 0.72) were less reliable. This indicated that the intraindividual variability was small and the interindividual reliability was high by using these measures in this cohort study. Laboratory variability did not significantly contribute to total variance (ICC > 0.95, p < 0.001). Other possible sources of variation such as bathing, showering, dishwashing and physical activities were unlikely to contribute significantly to total variance. For sampling strategies, 1-day blood sampling and 2-day urine sampling are sufficient to achieve reliability for an epidemiological study if a quasi-steady-state TCAA level in the body is reached. The results suggest that TCAA ingestion, TCAA loading in blood and urinary TCAA excretion are reliable measures for use as biomarkers in epidemiological studies.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".