Farm Childrenʼs Exposure to Herbicides
Bibliographic record
Abstract
BACKGROUND: Pesticide exposure has been associated with various childhood cancers. However, most studies rely on questionnaires, with few using biologic measures of dose. This study was designed to measure herbicide exposure directly in children of farm applicators, and to compare these results with exposure imputed from questionnaire information. METHODS: Two consecutive 24-hour urine samples were collected from 92 children of Ontario farm applicators who used the herbicides 2,4-D (2,4-dichlorophenoxyacetic acid) or MCPA (4-chloro-2-methylphenoxyacetic acid) for the first time during 1996. The farm applicator completed questionnaires describing his pesticide-handling practices as well as the child's location during the various stages of handling these pesticides. RESULTS: Approximately 30% of the children on farms using these herbicides had detectable concentrations in their urine, with maximum values of 100 microg/L for 2,4-D and 45 microg/L for MCPA. Children with higher levels were more likely to be boys and to have parents who also had higher mean urinary concentrations. The sensitivity and specificity of a simple indicator of use were 47% and 72%, respectively, for 2,4-D, and 91% and 30%, respectively, for MCPA, using the biomonitoring data as the gold standard. CONCLUSIONS: Information on living on a farm, or on living on a farm where a specific pesticide is used, is not enough to classify children's exposures. Given this potential for misclassification, we urge incorporation of biomonitoring studies in subsets of children at least to estimate the extent of misclassification.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".