Does the nicotine metabolite ratio moderate smoking cessation treatment outcomes in real‐world settings? A prospective study
Bibliographic record
Abstract
BACKGROUND AND AIMS: In smoking treatment trials comparing varenicline with transdermal nicotine replacement therapy (NRT), stratified by nicotine metabolite (3-hydroxycotinine/cotinine) ratio (NMR), the relative benefit of varenicline is greater among normal rather than slow metabolizers. This study tested if the relative effectiveness of varenicline and NRT is associated with NMR status in a natural treatment setting. A secondary aim was to test if this relationship is moderated by behavioural support. DESIGN: Prospective observational multi-centre study with 4-week and 52-week follow-up. SETTING: Nine English Stop Smoking Services (SSS). PARTICIPANTS: Data came from 1556 smokers (aged ≥ 16 years) attending SSS between March 2012 and March 2013. INTERVENTIONS: Participants received pharmacotherapy together with behavioural support. MEASUREMENTS: The primary outcome was carbon monoxide-verified continuous abstinence at both follow-up times. Main explanatory variables were (1) NMR status [slow (NMR < 0.31, n = 451) versus normal (NMR ≥ 0.31, n = 1105) metabolizers]; (2) pharmacotherapy (varenicline versus NRT) and (3) behavioural support (individual versus group-based treatment). Analyses adjusted for baseline socio-demographic, SSS, mental/physical health and smoking characteristics. FINDINGS: Of participants, 44.2% [95% confidence interval (CI) = 41.7-46.6%] and 8.0% (95% CI = 6.8-9.5%) were continuously abstinent at 4 and 52 weeks. Varenicline was more effective than NRT at 4 weeks (P < 0.001) but only marginally so at 52 weeks (P = 0.061). There was no or inclusive evidence that NMR status moderated relative efficacy of varenicline and NRT at 4- [P = 0.60, Bayes factor (BF) = 0.25] or 52-week follow-ups (P = 0.74, BF = 0.73). However, this relationship was moderated by behavioural support (p = 0.012): the relative benefit of varenicline over NRT at 52-week follow-up was greater in slow, not normal, metabolizers receiving group rather than individual support (P = 0.012). CONCLUSIONS: In a real-world setting, the nicotine metabolite ratio status of treatment-seeking smokers does not appear to contribute substantially to the differential effectiveness of varenicline and nicotine replacement therapy in Stop Smoking Services, when both pharmacotherapy and behavioural support are self-selected.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".