THERAPEUTICS FOR NICOTINE ADDICTION
Bibliographic record
Abstract
About 25 % of adults in the United States smoke tobacco cigarettes.Most continue smoking because they are addicted to nicotine. That nicotine is central to maintaining tobacco use is well established (49). When asked, 70 % of cigarette smokers report that they would like to quit. Each year, less than 1%will actually succeed without any therapeutic inter-ventions. Few other conditions in medicine present nicotine addiction’s mix of lethality, prevalence, cost, and relative therapeutic neglect, despite effective and readily available treatment interventions. Health care providers too often fail to assess or treat tobacco addiction despite substantial evi-dence that even brief therapeutic interventions are effective (2). Worldwide potential benefits of prevention and adequate treatment are staggering (96). More than 1.2 billion people regularly smoke tobacco. During the twentieth century, only approximately 0.1 billion people died of tobacco use–related illnesses. If current smoking patterns continue, 1 billion additional people will die of smoking-related illness during this century. Half will die during middle age. About 4 million people died of tobacco-related disease in 1998. Projections indicate 10 million tobacco-related deaths yearly by the year 2030, with 70 % of those deaths in devel-oping countries. Reducing the number of current smokers by 50 % would avoid 25 million premature deaths in the first quarter of this century and about 150 million more by midcentury (96). Understanding the role of nicotine in sustaining tobacco addiction offers a basis for optimal and rational treatments for preventing or stopping smoking (49). Nicotine addic-tion has much in common with other addictions, so consid-eration of therapeutics should help in development of thera-
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.048 | 0.017 |
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".