Pesticide exposure and cognitive decline in a rural South Korean population
Bibliographic record
Abstract
We aimed to investigate the relationship between pesticide exposure and cognitive decline in a rural South Korean population. From July 2015 to December 2017, 200 randomly selected Korean Farmers Cohort study participants were recruited and of these, 169 participants were analyzed. Pesticide exposure was investigated using a standardized questionnaire, and the Korean-Montreal Cognitive Assessment (K-MoCA) was conducted. Cognitive decline was more frequent among those directly exposed to pesticides (P = 0.057). Pesticide exposure and cognitive decline were positively correlated in the group with direct exposure versus no exposure (crude odds ratio [OR], 2.66; 95% confidence interval [CI], 1.17-6.04); this relationship was insignificant after adjustment (adjusted OR, 1.50; 95% CI, 0.57-3.92). There was a significant difference in the K-MoCA scores for each group based on pesticide exposure (P = 0.003). When we stratified by age, differences in the K-MoCA scores depending on the degree of pesticide exposure in the those aged 60 to 69 years were identified. Overall, there was a tendency towards an association between pesticide exposure and cognitive decline in rural Korean adult farmers. In our study, chronic pesticide exposure tended to have a greater impact in certain age group (60-69 years) than in those under 60 and over 70.
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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".