Early exposure to agricultural pesticides and the occurrence of autism spectrum disorder: a systematic review
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate the influence of early exposure to agricultural pesticides and their relationship with autism spectrum disorder. DATA SOURCE: This systematic review was registered at PROSPERO as CRD42020204842. The subject was systematically analyzed on PubMed, Scopus, and Web of Science databases until April 2021. Only studies with humans with early exposure to agricultural pesticides and diagnosis of autism were included. Exclusion criteria were studies on pesticides for domestic or veterinary use and late exposure. There were no language and time restriction. The quality analysis of the studies used the Newcastle-Ottawa Scale. DATA SYNTHESIS: Six case-control studies were included; three of them measured the route of exposure by maternal biomarkers and the others by the residence address. The studies had scores between moderate and high in the quality assessment tool. It was found high rates of association between early exposure to agricultural pesticides and autism and detection limit above the quantification for a sample of polychlorinated biphenyls, hexachlorobenzene, and dichlorodiphenyldichloroethylene. CONCLUSIONS: There is evidence concerning the exposure to agricultural pesticides in early life and the development of the autism spectrum disorder; however, more studies are required to better understand their possible association.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".