Chronic pesticide poisoning from persistent low-dose exposures in Ecuadorean floriculture workers: toward validating a low-cost test battery
Bibliographic record
Abstract
Chronic pesticide poisoning is difficult to detect. We sought to develop a low-cost test battery for settings such as Ecuador's floriculture industry. First we had to develop a case definition; as with all occupational diseases a case had to have both sufficient effective dose and associated health effects. For the former, using canonical discriminant analysis, we found that adding measures of protection and overall environmental stressors to occupational category and duration of exposure was useful. For the latter, factor analysis suggested three distinct manifestations of pesticide poisoning. We then determined sensitivity and specificity of various combinations of symptoms and simple neurotoxicity tests from the Pentox questionnaire, and found that doing so increased sensitivity and specificity compared to use of acethylcholinesterase alone--the current screening standard. While sensitivity and specificity varied with different case definitions, our results support the development of a low-cost test battery for screening in such settings.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".