Pesticide Exposures and Developmental Outcomes: The Epidemiological Evidence
Bibliographic record
Abstract
Since the advent of DDT as an insecticide in the late 1930s, billions of kilograms of pesticide active ingredient have been sold in North America and around the world. In recent years, there has been a heightened public awareness of pesticides and child health and a number of epidemiologic studies linked pre- and postnatal exposures to pesticides to a number of adverse developmental outcomes, including fetal death, intrauterine growth restriction, preterm birth, and birth defects. Given this, it was felt prudent to critically appraise the evidence for periconceptual pesticide exposures and developmental outcomes. The epidemiological evidence for specific pesticide classes, families, and active ingredients were examined and summarized and recommendations were made for how to improve future studies in order to address the current pitfalls and gaps in the studies in this area. Many of the studies suffered from poor exposure estimation, relying on job title only and/or the exposure category "any pesticide" as a measure of exposure, and there was limited or inadequate evidence to support causality for all associations examined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".