An Evaluation of 24-Hour Urinary Creatinine Excretion for Use in Identification of Incomplete Urine Collections and Adjustment of Absorbed Dose of Pesticides
Bibliographic record
Abstract
The accurate quantification of the absorbed dose of pesticides following occupational exposures generally requires complete 24-hour urine collections, often over extended periods of time. Difficulty in obtaining volunteer cooperation may result in incomplete urine collections. Traditionally, 24-hour urinary creatinine has been used to identify incomplete urine samples and has been used to standardize pesticide and other chemical dose estimates. More recently, the use of creatinine to standardize dose estimates has been questioned, as has its utility in the identification of incomplete urine collections. This research evaluates the use of personal observation, statistical methods, and published models to predict creatinine excretion to identify and adjust for incomplete urine collections. Based on the use of published creatinine prediction models, an evaluation of the day-to-day creatinine excretion within subjects, and personal observation, a small number of suspected urine samples were identified. Although it is likely that these samples were incomplete, correction of these urine volumes based on the published models did little to improve pesticide dose prediction. Further, results indicate that subjects who report missed urine samples may be able to estimate the missing volumes with some accuracy. In future pesticide exposure studies, the use of self-reported missed volumes may help to increase the accuracy of dose prediction when there is strong cooperation with collection procedures. A statistical model to predict creatinine excretion in professional turf applicators was developed to provide a preliminary screening for urinary completeness for future studies in which compliance with urinary collection is thought to be insufficient.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".