Nicotine metabolite ratio predicts efficacy of transdermal nicotine for smoking cessation
Bibliographic record
Abstract
BACKGROUND: Nicotine is metabolized to cotinine, and cotinine is metabolized to 3'-hydroxycotinine (3-HC) by the liver enzyme cytochrome P450 (CYP) 2A6. More rapid metabolism of nicotine may result in lower nicotine blood levels from nicotine replacement products and poorer smoking cessation outcomes. This study evaluated the utility of the 3-HC/cotinine ratio as a predictor of the efficacy of nicotine replacement therapy as an aid for smoking cessation. METHODS: By use of an open-label design, 480 treatment-seeking smokers were randomly assigned to 8 weeks of transdermal nicotine or nicotine nasal spray use, plus behavioral group counseling. Assessments included demographics, smoking history, body mass index, and plasma nicotine, cotinine, and 3-HC concentrations, as well as CYP2A6 genotypes. Smoking cessation was biochemically verified at the end of treatment and at 6-month follow-up. RESULTS: The rate of nicotine metabolism, as indicated by pretreatment 3-HC/cotinine ratio derived from cigarette smoking, predicted the effectiveness of transdermal nicotine at both time points. The odds of abstinence were reduced by almost 30% with each increasing quartile of metabolite ratio (odds ratio, 0.72 [95% confidence interval, 0.57-0.90]; P=.005). Higher metabolite ratios also predicted lower nicotine concentrations (beta=-1.72, t(179)=-3.31, P<.001), as well as more severe cravings for cigarettes after 1 week of treatment (beta=0.32, t(190)=2.91, P=.004). The metabolite ratio did not predict cessation with use of nicotine nasal spray (odds ratio, 1.05 [95% confidence interval, 0.83-1.33]; P=.68). CONCLUSION: The nicotine metabolite ratio might be useful in screening smokers to determine likely success with a standard dose of transdermal nicotine.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".