Drug Use, Abuse, and Dependence and the Persistence of Nicotine Dependence
Bibliographic record
Abstract
INTRODUCTION: Illicit drug use and nicotine dependence (ND) frequently co-occur. Yet, to date very few studies have examined the role of alcohol and illicit drug use in ND persistence. The objectives of this study were to investigate the relationships between specific classes of drug use, abuse, and dependence and the persistence of ND over time among adults in the United States. METHODS: Data were drawn from the National Epidemiologic Survey on Alcohol and Related Conditions, a national survey of 34,653U.S. adults interviewed between 2001-2002 and reinterviewed 3 years later. Logistic regression analyses were used to investigate the relationships between various classes of drug use, abuse, and dependence among adults with ND at Wave 1 and the odds for persistent ND at Wave 2. Analyses were adjusted for differences in demographic characteristics, mood/anxiety disorders, alcohol use disorders, and other substance use disorders. RESULTS: Lifetime drug use was not associated with significantly increased likelihood for persistent ND. Sedative abuse was associated with increased odds for nicotine persistence, but no other types of drug abuse were predictive of ND persistence, after adjusting for demographics, mood/anxiety, and alcohol use disorders. All types of drug dependence were associated with persistence of ND; the strongest associations emerged between opioid and tranquilizer dependence and persistent ND, while the associations between cannabis and cocaine dependence were no longer significant after adjusting for mood/anxiety disorders. CONCLUSIONS: Clinicians should take care to evaluate the presence and/or history of drug dependence among patients seeking treatment for smoking cessation. These data suggest that a history of substance dependence predicts increased vulnerability to persistent ND.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".