Antibiotic Body Burden of Chinese School Children: A Multisite Biomonitoring-based Study
Bibliographic record
Abstract
To explore the antibiotic body burden of Chinese school children, total urinary concentrations (free and conjugated) of 18 representative antibiotics (5 macrolides, 2 β-lactams, 3 tetracyclines, 4 quinolones, and 4 sulfonamides) were measured by ultraperformance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry among 1064 school students recruited from 3 economically and geographically distinct areas in east China in 2013. All 18 antibiotics were detected in urine samples with the detection frequencies ranging from 0.4 to 19.6%. The antibiotics were detected in 58.3% of urine samples overall, and this detection frequency reached at 74.4% in one study site. Of them, 47.8% of the urine samples had a sum of mass concentration of all antibiotics between 0.1 (minimum) and 20.0 ng/mL, and 8 antibiotics had their concentrations of above 1000 ng/mL in some urine samples. Three veterinary antibiotics, 4 human antibiotics, and 11 human/veterinary antibiotics were found overall in 6.3, 19.9, and 49.4% of urine samples, respectively. The detection frequencies and concentration levels of antibiotics in urine samples differed by study areas. Concerning mixed exposures, a total of 137 combinations of antibiotics and 20 combinations of antibiotic categories were found overall. Two or more antibiotics or categories were concurrently detected in more than 20% of urine samples. On the basis of a usage analysis, contaminated food or environment might be relevant exposure sources for tetracyclines, quinolones, and sulfonamides.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".