Urinary Antibiotics of Pregnant Women in Eastern China and Cumulative Health Risk Assessment
Bibliographic record
Abstract
Exposure to antibiotics during pregnancy can pose a systematic effect on human health. A few biomonitoring studies have demonstrated an extensive exposure of children to antibiotics, but there is still a lack of data for pregnant women. To assess the exposure of pregnant women to antibiotics and potential health risk, we investigated 536 pregnant women aged 16-42 years from two geographically different study sites in Eastern China in 2015. We measured 21 antibiotics of five categories (seven fluoroquinolones, three phenicols, four tetracyclines, three macrolides, and four sulfonamides) in urine using the isotope dilution ultraperformance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. The hazard index (HI) was calculated on the basis of estimated daily exposure dose and acceptable daily intakes. A total of 16 antibiotics were found in urine, with detection frequencies between 0.2 and 16.0%. Antibiotics were overall detected in 41.6% of urine, and two or more antibiotics were detected in 13.1% of urine. Ciprofloxacin, ofloxacin, and trimethoprim were most frequently detected in urine, with detection frequencies between 10 and 20%. The majority of the antibiotics tested had an estimated daily exposure dose less than 1 μg/kg/day, and 4.3% of pregnant women had a HI value of more than 1. These findings indicated that pregnant women were frequently exposed to antibiotics and some individuals were in the potential risk of adverse microbiological effects induced by antibiotics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".