A comparison of risk factors for cigarette and e-cigarette use in the United States adult population.
Bibliographic record
Abstract
1546 Background: The US CDC and public health agencies have reported alarming increases in e-cigarette (ecig) use among youth, even as cigarette (cig) use among youth decline. In this study, other risk factors for cig and ecig use are compared. Methods: This study used data from the Health Information National Trends Survey 5 Cycle 1 survey, conducted in 2017. Univariate survey-weighted logistic regression analyzed responses as a nationally representative US population. Results: Inverted trends included being 35 or older (cig: OR=1.22, p<0.01; ecig: OR=0.79, p<0.01), being a student (cig: OR=0.77, p<0.01; ecig: OR=1.24, p<0.01) or retired (cig: OR=1.09, p<0.01; ecig: OR=0.89, p<0.01) compared to being employed, and being single (cig: OR=0.92, p<0.03; ecig: OR=1.18, p<0.01). Having considered quitting smoking was not significantly associated with ecig use. Conclusions: Segments of the US adult population educated with anti-tobacco campaigns may remain at increased risk for ecig use.[Table: see text]
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".