Collaborative Method Performance Study of the Measurement of Nicotine, Its Metabolites, and Total Nicotine Equivalents in Human Urine
Bibliographic record
Abstract
Abstract Background: Biomarkers of tobacco exposure have a central role in studies of tobacco use and nicotine intake. The most significant exposure markers are nicotine itself and its metabolites in urine. Therefore, it is important to evaluate the performance of laboratories conducting these biomarker measurements. Methods: This report presents the results from a method performance study involving 11 laboratories from 6 countries that are currently active in this area. Each laboratory assayed blind replicates of seven human urine pools at various concentrations on three separate days. The samples included five pools blended from smoker and nonsmoker urine sources, and two additional blank urine samples fortified with pure nicotine, cotinine, and hydroxycotinine standards. All laboratories used their own methods, and all were based on some form of liquid chromatography/tandem mass spectrometry. Results: Overall, good agreement was found among the laboratories in this study. Intralaboratory precision was good, and in the fortified pools, the mean bias observed was < + 3.5% for nicotine, approximately 1.2% for hydroxycotinine, and less than 1% for cotinine (1 outlier excluded in each case). Both indirect and direct methods for analyzing the glucuronides gave comparable results. Conclusions: This evaluation indicates that the experienced laboratories participating in this study can produce reliable and comparable human urinary nicotine metabolic profiles in samples from people with significant recent exposure to nicotine. Impact: This work supports the reliability and agreement of an international group of established laboratories measuring nicotine and its metabolites in urine in support of nicotine exposure studies. Cancer Epidemiol Biomarkers Prev; 27(9); 1083–90. ©2018 AACR.
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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 | 0.111 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".