Effects of prenatal environmental exposure to pesticides on the neurodevelopment of children in the department of Alto Paraná Paraguay at 34-36 months of age
Bibliographic record
Abstract
Prenatal exposure to pesticides of different chemical groups is associated with an increased risk of anthropometric, neurological, and neurodevelopmental alterations in children. The paper reports an evaluation of the neurodevelopmental, nutritional, and sensory status of a group of children belonging to birth cohorts with and without prenatal environmental exposure to pesticides, in the department of Alto Paraná, from a previously published study. A prospective observational study was conducted. The neurodevelopmental status was evaluated with the Battelle Developmental Inventory Screening test (BDIST). The sensory status was assessed through an ophthalmological examination and an otoacoustic emissions test. A total of 100 children aged 34 to 36 months, of whom only 50 were exposed to pesticides during the prenatal period, were included in the study. Results showed that 45% of the children studied had neurodevelopmental impairments. The cognitive area was the most affected (56%) and the risk of abnormal neurodevelopment was 5.6 times higher in children who had been prenatally exposed to pesticides than in the unexposed. Malnourished children were 4 times more likely to have an abnormal BDIST result. Results suggest that prenatal pesticide exposure is an important risk factor for lower neurodevelopment in children at 34 to 36 months of age, adjusted for socioeconomic factors.
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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 | 0.000 | 0.000 |
| 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".