Assessing the Relationship between Biomarkers of Exposure and Biomarkers of Potential Harm: PATH Study Wave 1 (2013 to 2014)
Bibliographic record
Abstract
BACKGROUND: The adequacy of biomarkers of potential harm (BOPH) for assessing tobacco products was explored based on their ability to distinguish tobacco use from non-use, change with cessation, and to show biological gradient. METHODS: The sample included individuals with biomarker data in wave 1 of the Population Assessment of Tobacco Health study who never used tobacco, currently smoke cigarettes exclusively, used to smoke cigarettes exclusively (quit in past 12 months), currently use smokeless tobacco exclusively, and currently use e-cigarettes exclusively. We compared BOPH levels between groups and assessed the relationships between log-transformed biomarkers of exposure [BOE; total nicotine equivalents including seven nicotine metabolites (TNE-7), 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanonol (NNAL), N-acetyl-S-(2-cyanoethyl)-L-cysteine, 1-hydroxypyrene, cadmium, and serum cotinine (SCOT)], and BOPH [high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), fibrinogen, soluble intercellular adhesion molecule-1 (sICAM-1), and 8-isoprostane]. RESULTS: Among people who smoke, both sICAM-1 and 8-isoprostane distinguished smoking from non-use and were associated with all six BOE. Among people who use smokeless tobacco, 8-isoprostane was associated with TNE-7 and NNAL whereas hs-CRP was associated with SCOT. Among people who use e-cigarettes, no associations between BOPH and BOE were observed. CONCLUSIONS: Both sICAM-1 and 8-isoprostane may be useful for assessing the use or changes in use of some tobacco products. Studies examining their predictive validity could further strengthen our understanding of these two biomarkers. IMPACT: We found that two biomarkers of potential harm, soluble intercellular adhesion molecule-1 and 8-isoprostane, may have utility in studies assessing the potential harm of tobacco use in absence of long-term epidemiological studies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".