A Tool to Identify Adolescents at Risk of Cigarette Smoking Initiation
Bibliographic record
Abstract
OBJECTIVES: To describe the development of a prognostic tool to identify adolescents at risk for transitioning from never to ever smoking in the next year. METHODS: Data were drawn from the Nicotine Dependence in Teens study, a longitudinal investigation of adolescents (1999 to present). A total of 1294 students initially age 12 to 13 years were recruited from seventh-grade classes in 10 high schools in Montreal. Self-report questionnaire data were collected every 3 months during the 10-month school year over 5 years (1999–2005) until participants completed high school (n = 20 cycles). Prognostic variables for inclusion in the multivariable analyses were selected from 58 candidate predictors describing sociodemographic characteristics, smoking habits of family and friends, lifestyle factors, personality traits, and mental health. Cigarette smoking initiation was defined as taking even 1 puff on a cigarette for the first time, as measured in a 3-month recall of cigarette use completed in each cycle. RESULTS: The cumulative incidence of cigarette smoking initiation was 16.3%. Data were partitioned into a training set for model-building and a testing set to evaluate the performance of the model. The final model included 12 variables (age, 4 worry or stress-related items, 1 depression-related item, 2 self-esteem items, and 4 alcohol- or tobacco-related variables). The model yielded a c-statistic of 0.77 and had good calibration. CONCLUSIONS: This short prognostic tool, which can be incorporated into busy clinical practice, was used to accurately identify adolescents at risk for cigarette smoking initiation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".