Dicamba and 2,4-D in the Urine of Pregnant Women in the Midwest: Comparison of Two Cohorts (2010–2012 vs. 2020–2022)
Bibliographic record
Abstract
Currently, there are no known human biomonitoring studies that concurrently examine biomarkers of dicamba and 2,4-D. We sought to compare biomarkers of exposure to herbicides in pregnant women residing in the US Midwest before and after the adoption of dicamba-tolerant soybean technology using urine specimens obtained in 2010–2012 from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (N = 61) and in 2020–2022 from the Heartland Study (N = 91). Specific gravity-standardized concentration levels for each analyte were compared between the cohorts, assuming data are lognormal and specifying values below the LOD as left-censored. The proportion of pregnant individuals with dicamba detected above the LOD significantly increased from 28% (95% CI: 16%, 40%) in 2010–2012 to 70% (95% CI: 60%, 79%) in 2020–2022, and dicamba concentrations also significantly increased from 0.066 μg/L (95% CI: 0.042, 0.104) to 0.271 μg/L (95% CI: 0.205, 0.358). All pregnant individuals from both cohorts had 2,4-D detected. Though 2,4-D concentration levels increased, the difference was not significant (p-value = 0.226). Reliance on herbicides has drastically increased in the last ten years in the United States, and the results obtained in this study highlight the need to track exposure and impacts on adverse maternal and neonatal outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".