Nicotine metabolite ratio and smoking outcomes using nicotine replacement therapy and varenicline among smokers with and without psychiatric illness
Bibliographic record
Abstract
INTRODUCTION: It has been suggested that the effectiveness of nicotine replacement smoking cessation pharmacotherapy may be enhanced by assessing rates of nicotine metabolism using the nicotine metabolite ratio - which reflects differences in the activity of the CYP2A6 hepatic enzyme - and titrating doses appropriately. To date, supporting evidence is equivocal, with little information regarding the assessment and effectiveness of the nicotine metabolite ratio among smokers with psychiatric conditions. METHODS: The nicotine metabolite ratio of 499 smokers from the FLEX trial was determined using urine samples obtained at baseline. They were randomized to receive either: standard transdermal nicotine (nicotine replacement therapy); extended nicotine replacement therapy + adjunct nicotine agent; or varenicline. Primary cessation outcomes were seven-day point prevalence at 5, 10, 22, and 52 weeks post-target quit date, comparing across treatment and psychiatric status. Our principal analysis employed logistic regression (outcome: abstinence), using slow metabolizers as the reference category. RESULTS: No differences were observed by nicotine metabolite ratio classification (slow, moderate, fast) with respect to any demographic or smoking-related variables. Nicotine metabolite ratio class did not predict smoking cessation in either the overall sample, or by treatment condition at any time-point (week 52 moderate metabolizers: odds ratio 1.34, 95% confidence interval (0.68-2.63), p=0.394; fast metabolizers: odds ratio 1.04 (0.56-1.91), p=0. 906). CONCLUSION: Our results did not find any associations between nicotine metabolite ratio and cessation outcomes among smokers using nicotine replacement therapy or varenicline with and without lifetime psychiatric conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".