Understanding the implications of the biobehavioral basis of nicotine addiction and its impact on the efficacy of treatment
Bibliographic record
Abstract
INTRODUCTION: Tobacco use remains the leading cause of preventable death in the United States. There are efficacious behavioral and pharmacological options for smoking cessation including three FDA approved therapies - nicotine replacement therapy, varenicline and bupropion. Nevertheless, uptake of smoking cessation treatments continues to be poor and there is a need for novel smoking cessation treatments. Areas covered: This article reviews the biobehavioral basis of nicotine addiction, its implications for smoking cessation treatments, the various neurotransmitter systems involved in nicotine addictive effects, and their potential therapeutic value. Included are discussions around the role of genetic factors in predicting response to pharmacotherapy and what we know about appropriate application of pharmacotherapy and behavioral interventions for tobacco use disorder. The evidence for harm reduction measures in individuals who are not willing or able to quit smoking is also reviewed. Expert commentary: Many neurotransmitter system targets have been investigated as a result of our understanding of the underlying neurobiology of tobacco use disorder, and there remain important targets that have yet to be fully explored. rTMS or combination therapies are proposed as possible novel strategies to improve smoking cessation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".