Characterising the nicotine metabolite ratio and its association with treatment choice: A cross sectional analysis of Stop Smoking Services in England
Bibliographic record
Abstract
Pharmacotherapy provision based on Nicotine Metabolite Ratio (NMR) status (slow/normal metabolism) may improve smoking cessation rates. However, it is unclear whether NMR status is consistent across patient characteristics and current treatment choice. Data come from 1,826 participants attending Stop Smoking Services (SSS) across England in 2012/13. Sociodemographic, mental/physical health, smoking and treatment characteristics (nicotine replacement therapy vs. other pharmacotherapy; group vs. one-to-one behavioural support) were assessed. Salivary nicotine metabolites were measured and NMR (3-hydroxycotinine/cotinine) computed, characterising smokers as slow (NMR < 0.31) or normal (NMR ≥ 0.31) metabolisers. Normal metabolisers were older than slow metabolisers (Odds Ratio (OR) = 1.49, 95% Confidence Interval (CI) = 1.32-1.69) but no other characteristics were associated with NMR status. Overall, predictors accounted for only 7.3% of NMR variance. In adjusted analysis, pharmacotherapy type was not associated with NMR status, but normal metabolisers were less likely to use group support (OR = 0.67, 95% CI = 0.51-0.89). NMR status does not vary substantially across sociodemographic characteristics. Given its impact on pharmacotherapy efficacy, the lack of an association with pharmacotherapy choice suggests there is scope to use NMR status to optimise the selection and efficacy of smoking cessation pharmacotherapy. The unexpected association of NMR status with behavioural support should be explored further.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".