Screening Tools for Who Will Start Smoking and the Future of Clinical Prediction
Bibliographic record
Abstract
Is the lack of a prognostic tool to assist clinicians in identifying youth at risk of transitioning from never to ever smoking a major barrier to counseling? Sylvestre et al1 address this in a longitudinal study of 12- to 13-year-olds in Montreal. They start with 58 candidate items on youth tobacco use initiation and monitor these early adolescents closely from 1999 to 2005, finding that 12 variables (age, 4 worry or stress items, 1 depression item, 2 self-esteem items, and 4 alcohol or tobacco items) are a best fit in predicting which youth go on to be among the ∼16% who were nonsmokers at baseline and then puffed on a cigarette during the coming year. The authors’ statistical analysis and use of modeling are elegant, and they call attention to an important and often overlooked fact that a first puff of a cigarette is a sentinel event that can rapidly lead to nicotine dependence and sustained smoking. However, the assumptions made by the authors in the question they posed, and thus the tool they developed, raise concerns that … Address correspondence to Jonathan D. Klein, MD, MPH, FAAP, Department of Pediatrics, University of Illinois at Chicago, 840 S Wood St, MC 856, Chicago, IL 60612. E-mail: jonklein{at}uic.edu
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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 | 0.005 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".