278Sources and health risk of exposure to neonicotinoids in Chinese children: A biomonitoring-based stud
Bibliographic record
Abstract
Abstract Background Recent studies have suggested an extensive exposure to neonicotinoids in human, but the sources and health risk of exposure to neonicotinoids remains little known in children. Methods After 309 school children were selected in 2019 from a dynamic child cohort established in Shanghai, East China, detailed data about basic information, intake of drinking water, and food consumption were collected and 12 typical neonicotinoids and metabolites were determined in morning urine. Sources of exposure to neonicotinoids were explored by binary/ordinal logistic regression model. Health risk was assessed by hazard quotient (HQ) and hazard index (HI). Results Except for imidaclothiz, thiacloprid, and 5-OH-imidacloprid, other six neonicotinoids and three metabolites were detected in 81.3% of urine samples overall. After adjusted for potential confounders, Thiamethoxam was more detected in children consuming more fresh vegetables (odds ratio (OR): 2.93, 95% confidence interval (CI): 1.21,7.14) and its subgroup (Chinese cabbage (OR: 2.00, 95%CI: 0.89,4.46) and nori and kelp (OR: 2.25, 95%CI:1.21,4.17)). Clothianidin showed a similar association with fresh vegetables and its subgroup to thiamethoxam. N-desmethyl-acetamiprid were detected in children drinking type water more (OR: 1.84, 95%CI: 1.10,3.06). The maximum HQ and HI were 0.3522 and 0.5187, respectively, and 2.8% of children had HIs between 0.1 and 1. Conclusions Tap water and fresh vegetables were potential exposure sources. A low health risk was posed on Chinese children by neonicotinoids. Given limited data about the adverse effects of neonicotinoids on human, sufficient attention should be paid to the exposure to these compounds and potential health effects. Key messages Tap water and fresh vegetables were exposure sources and a low health risk was posed on Chinese children by neonicotinoids.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".