Risk factors for tobacco dependence in adolescent smokers
Bibliographic record
Abstract
OBJECTIVE: To study the incidence of conversion to tobacco dependence (TD) and the prevalence of the TD state in relation to several potential determinants in a sample of adolescent smokers. METHODS: Questionnaires were administered every 3-4 months to document TD symptoms, amount of cigarette consumption, and depression symptoms in a prospective cohort of 1293 grade 7 students in a convenience sample of 10 schools. RESULTS: Over 54 months of follow-up, 113 of 344 novice smokers converted to TD. The referent series for the analysis of incidence comprised 823 person-surveys. The prevalence series included 1673 person-surveys, contributed by 429 smokers. Conversion to TD and TD status were associated with the intensity of recent (that is, past 3-month) cigarette consumption (adjusted incidence rate ratio (aIRR) 1.63 (95% confidence interval (CI) 1.36 to 1.97) and adjusted prevalence odds ratio (aPOR) 1.35 (95% CI 1.23 to 2.48) per 100 cigarettes per month), slowest CYP2A6 activity (aIRR 4.19 (95% CI 1.38 to 12.76) and aPOR 2.30 (95% CI 1.29 to 4.09)), depression score (aIRR 1.61 (95% CI 1.17 to 2.21) and aPOR 1.47 (95% CI 1.22, 1.75) per 1-unit change). Additional determinants included, for conversion to TD, time since onset of cigarette use (aIRR 0.76 (95% CI 0.58 to 1.00) per year) and, for the TD state, positive TD status six months ago (aPOR 3.53 (95% CI 2.41 to 5.19)). CONCLUSIONS: TD risk in adolescents is associated with intensity of recent cigarette consumption, while the role of more distant cigarette consumption appears small; subjects with slow nicotine metabolism and those with more depression symptoms are at increased risk of becoming tobacco dependent. The risk of being tobacco dependent is considerably higher in subjects who had previously developed the TD state.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".