2031 – Analysis Of Nicotinic Receptor Genes In Nicotine Replacement Treatment
Bibliographic record
Abstract
Nicotine replacement therapy (NRT) in the form of patches, gum or inhaler is the most popular treatment for quitting smoking. However, NRT is effective for only a fraction of smokers. Therefore, research is needed to maximize the successful treatment of smokers who want to quit. The purpose of this study is to examine the role of inherited genetic variation in the therapeutic response to NRT. The specific aim of this project is to examine the role of genetic variation in the alpha5 nicotine receptor subunit in NRT treatment success or failure. This nicotine receptors are the main sites of action of nicotine that contribute to tobacco dependence. Study subjects were 403 participants in a stop smoking study examining the effectiveness of different types of NRT on smoking cessation. Subjects received 10-weeks of NRT treatment and their quit success was assessed at the end-treatment. The DNA extracted from blood samples was tested for the D398N variation in the gene that encode for the nicotinic receptors α5. We found a slight trend (p = 0.107) for the allele N398 associated with treatment response (44% in quitting group versus 37% in the relapsing group). This study is one of the first to investigate the link between treatment success with NRT and the genetics of nicotine receptor in the Ontario population. This study confirms the link between CHRNA5 and nicotine dependence, however replications in larger samples are necessary to confirm that this marker will provide an evidence-base for individualizing treatment in smoking cessation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.008 | 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".