Adjusting urinary chemical biomarkers for hydration status during pregnancy
Bibliographic record
Abstract
One way of assessing a population's exposure to environmental chemicals is by measuring urinary biomarker concentrations, which can vary depending on the hydration status of the individual. The physiological changes that occur during pregnancy can impact the hydration adjustment approaches, such as calculating the individual's urinary flow rate (UFR), or adjusting concentrations using specific gravity (SG) or creatinine. A total of 1260 serial spot urine samples were collected from 80 women, averaging 32.4 years of age, throughout and shortly after pregnancy. The relationship between each approach was examined and time of day and across pregnancy differences were tested using linear mixed models. The correlation between the calculated excretion rate and each of the adjustment techniques was examined on a selection of seven phthalate metabolites. Based on the linear mixed model results, we found that UFR and creatinine excretion rates differed systematically across the population, with respect to body mass index (BMI) and time. SG differed with respect to BMI, but there were no systematic time trends. SG had the highest within-person reproducibility, according to the intraclass correlation coefficient (ICC). The excretion rate of each of the phthalates was most strongly correlated with the SG-standardized concentration. This analysis showed that SG showed a slightly better within-person reproducibility and the least amount of systematic variation when compared to creatinine adjustment. Therefore, SG correction appears to be a favorable approach for correcting for the hydration status of the pregnant women from this cohort.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".