Investigating the Impact of Genetics on the Effectiveness of Bupropion and Varenicline Treatment for Smoking Cessation
Bibliographic record
Abstract
Genetics plays a significant role in smoking behaviours and abstinence. In this genetics sub-study,we evaluated treatment-seeking Caucasian smokers (n =374) receiving bupropion (n = 180) or varenicline (n = 194) for smoking cessation. Genetic variants involved in nicotine neurobiology (CHRNA4, CHRNA5, CHRNB4 ) and the dopamine reward pathway (DRD2, SLC6A3 ) displayed nominal associations with abstinence. Additionally, DRD2 and COMT variants were nominally associated with cigarettes per day and nicotine dependence in the whole sample (n =374). However, genome-wide variants showed no significant associations with abstinence, or baseline smoking behaviours. Polygenic risk scores for smoking cessation (current versus former smoker) derived from the GWAS and Sequencing Consortium of Alcohol and Nicotine Use (GSCAN) meta-analysis sample, significantly predicted abstinence at the end of treatment and explained 2.188% of the variance for abstinence. These findings highlight the influence of genetic variants on smoking and the potential for personalized cessation strategies.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".