A review of the analysis of biomarkers of exposure to tobacco and vaping products
Bibliographic record
Abstract
Quantification of exposure to different chemicals from both combustible cigarettes and vaping products is important in providing information on the potential health risks of these products. To assess the exposure to tobacco products, biomarkers of exposure (BOEs) are measured in a variety of biological matrices. In this review paper, current knowledge on analytical methods applied to the analysis of biomarkers of exposure to tobacco products is discussed. Numerous sample preparation techniques are available for the extraction and sample clean up for the analysis of BOEs to tobacco and nicotine delivery products. Many tobacco products-related exposure biomarkers have been analyzed using different instrumental techniques, the most common techniques being gas and liquid chromatography coupled with mass spectrometry (GC-MS, GC-MS/MS and LC-MS/MS). To assess exposure to emerging tobacco products and study exposure in dual tobacco users, the list of biomarkers analyzed in urine samples has been expanded. Therefore, the current state of the literature can be used in preparing a preferred list of biomarkers based on the aim of each study. The information summarized in this review is expected to be a handy tool for researchers involved in studying exposures to tobacco products, as well as in risk assessment of biomarkers of exposure to vaping products.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".