Occupational pesticide exposure and cognitive impairment among adult farmers in northern Thailand
Bibliographic record
Abstract
Background Thai farmers are directly exposed to pesticides, which may result in adverse effects including cognitive impairment. Objective The aim of this study was to examine the association between occupational pesticide exposure and cognitive decline among adult farmers in northern Thailand. Material and Methods This cross-sectional study included 303 pesticide-using farmers over the age of 50 from Doi Tao District in Chiang Mai Province. Pesticide exposure score was calculated using an algorithm that considered personal protective equipment (PPE) scores and exposure intensity scores, as well as lifetime application days. The scores were classified as high or low exposure based on their median. The Thai version of the Montreal Cognitive Assessment (MoCA) test was used to assess cognitive function. Results The mean age of adult farmers was 58.74 years. The prevalence of cognitive impairment was 93.7%, with an average score of 19.6. Spearman’s rank correlation coefficient showed that the MoCA score was adversely correlated with lifetime application days (rs = -0.145), PPE score (rs = -0.163), exposure intensity score (rs = -0.184), and pesticide exposure score (rs = -0.225). Linear regression revealed that high exposed farmers had significantly lower MoCA scores than low exposed farmers, as measured by PPE score (B = -0.75; 95% CI: -1.46, -0.05), exposure intensity score (B = -0.97; 95% CI: -1.66, -0.27), and pesticide exposure score (B = -0.77; 95% CI: -1.47, -0.06), after controlling for sex, age, education, income sufficiency, and body mass index. Conclusions Thai farmers are at risk of cognitive impairment linked to occupational pesticide exposure, depending on their PPE use and exposure intensity. There is still a critical need for action to reduce the risk of negative health effects from pesticide exposure among Thai farmers.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".