Urinary Cotinine and Cotinine + Trans-3′-Hydroxycotinine (TNE-2) Cut-points for Distinguishing Tobacco Use from Nonuse in the United States: PATH Study (2013–2014)
Bibliographic record
Abstract
BACKGROUND: Determine the overall, sex-, and racially/ethnically-appropriate population-level cotinine and total nicotine equivalents (TNE-2, the molar sum of the two major nicotine metabolites) cut-points to distinguish tobacco users from nonusers across multiple definitions of use (e.g., exclusive vs. polytobacco, and daily vs. non-daily). METHODS: Using Wave 1 (2013-2014) of the U.S. Population Assessment of Tobacco and Health (PATH) Study, we conducted weighted Receiver Operating Characteristic (ROC) analysis to determine the optimal urinary cotinine and TNE-2 cut-points, stratified by sex and race/ethnicity. RESULTS: For past 30-day exclusive cigarette users, the cotinine cut-point that distinguished them from nonusers was 40.5 ng/mL, with considerable variation by sex (male: 22.2 ng/mL; female: 43.1 ng/mL) and between racial/ethnic groups (non-Hispanic other: 5.2 ng/mL; non-Hispanic black: 297.0 ng/mL). A similar, but attenuated, pattern emerged when assessing polytobacco cigarette users (overall cut-point = 39.1 ng/mL, range = 5.5 ng/mL-80.4 ng/mL) and any tobacco users (overall cut-point = 39.1 ng/mL, range = 4.8 ng/mL-40.0 ng/mL). Using TNE-2, which is less impacted by racial differences in nicotine metabolism, produced a comparable pattern of results although reduced the range magnitude. CONCLUSIONS: Because of similar frequency of cigarette use among polytobacco users, overall cut-points for exclusive cigarette use were not substantially different from cut-points that included polytobacco cigarette use or any tobacco use. Results revealed important differences in sex and race/ethnicity appropriate cut-points when evaluating tobacco use status and established novel urinary TNE-2 cut-points. IMPACT: These cut-points may be used for biochemical verification of self-reported tobacco use in epidemiologic studies and clinical trials.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".