Myopia and Exposure to Organophosphate and Pyrethroid Pesticides in the General United States Population
Bibliographic record
Abstract
Purpose: Previous research suggests that exposure to pesticides might be associated with human myopia, although data were obtained only from highly exposed individuals. The present study aimed to assess whether exposure to organophosphates and pyrethroids in the United States general population was associated with the prevalence of myopia. Methods: Data were obtained from the National Health and Nutrition Examination Survey (NHANES, years 1999-2008). One-spot urine samples were used to estimate the concentration of several pesticide metabolites. Exposure data and equivalent spherical refraction errors were available for 5147 and 2911 individuals for organophosphates and pyrethroids, respectively. Multiple logistic regression models were used to assess the relation between log10-transformed urinary levels of pesticide metabolites and the risk of moderate (≤-1 and >-5 diopters [D]) and high myopia (≤-5 D) in adolescents (12- to 19-years old) and young adults (20- to 40-years old). Models were adjusted for sex, age, ethnicity, diabetes, creatinine, cadmium and lead concentrations, and income in both age groups, but also for education level and cigarette and alcohol consumption in the adult group. Results: No association between organophosphates or pyrethroid metabolites and myopia was observed. However, after adjusting for education level and cigarette and alcohol consumption, a statistically significant decreased risk of high myopia in those with a 10-fold increase of dialkyl phosphate metabolites was found in adults but only in men (P < 0.05). Conclusions: Our results suggest that exposure to organophosphates or pyrethroids do not increase the risk of myopia in the United States general population.
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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 | 0.000 | 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".