A state‐of‐the‐science review of analytical methods for urinary dialkylphosphate metabolites in the assessment of exposure to organophosphate pesticides: From 2000 to 2022
Bibliographic record
Abstract
The general population and workers are exposed to organophosphate insecticides, one of the leading chemical classes of pesticides used in rural and urban areas. This paper aims to conduct an integrative review of the most used analytical methods for identifying and quantifying dialkylphosphate-which are metabolites of organophosphate insecticides-in the urine of exposed workers, discussing their advantages, limitations and applicability. Searches utilized the PubMed, the Scientific Electronic Library Online and the Brazilian Digital Library of Theses and Dissertations databases between 2000 and 2021. Twenty-five studies were selected. The extraction methods most used were liquid-liquid extraction (LLE) (36%) and solid-phase extraction (SPE) (36%), with the SPE being more economical in terms of time and amount of solvents needed, and presenting the best percentage of recovery of analytes, when compared with LLE. Nineteen studies (76%) used the gas chromatography method of separation, and among these, 12 records (63%) indicated mass spectrometry used as a detection technology (analyzer). Studies demonstrate that dialkylphosphates are sensitive and representative exposure biomarkers for environmental and occupational organophosphate exposure.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".