Cigarette ignition propensity, smoking behavior, and toxicant exposure: A natural experiment in Canada
Bibliographic record
Abstract
BACKGROUND: This study used a 'pre-post' research design to measure the impact of the Canadian reduced ignition propensity law on cigarette toxicity and smoking behavior among Canadian smokers. METHOD: The study was conducted in Ontario, Canada over a ten-month period in 2005-2006, consisting of 4 laboratory visits (baseline N = 61, final N = 42). At Visit 1, questionnaire data and biospecimens were collected. During the following 24 hours, participants smoked 5 cigarettes ad libitum through a topography recording device and collected their cigarette butts. Visit 2 consisted of a questionnaire and smoking one cigarette to measure laboratory topography values. After ten months, these procedures were repeated. RESULTS: Generalized estimating equations, with law status (pre and post) as a fixed within-subject factor, were used to determine changes in behavior and biomarker exposure. Overall, there were no significant differences in smoking topography, breath carbon monoxide, and saliva cotinine pre-post law (p>0.1). However, analyses revealed a significant increase in the summed concentrations of hydroxyfluorene metabolites (N = 3),, and 1-hydroxypyrene in urine, with at notable increase in hydroxyphenanthrene metabolites (N = 3) (pΣhydroxyfluorene = 0.013, 22% increase; p1-hydroxypyrene = 0.018, 24% increase; pΣhydroxyphenanthrene = 0.061, 17% increase). CONCLUSION: While the results suggest no change in topography variables, data showed increases in exposure to three PAH biomarkers following reduced ignition propensity implementation in Canada. These findings suggest that human studies should be considered to evaluate policy impacts.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".