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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".