Cigarette pack size and consumption: a randomized cross‐over trial
Bibliographic record
Abstract
BACKGROUND AND AIMS: Smoking fewer cigarettes per day may increase the chances of stopping smoking. Capping the number of cigarettes per pack is a promising policy option, but the causal impact of such a change is unknown. This study aimed to test the hypothesis that lowering cigarette pack sizes from 25 to 20 reduces the number of cigarettes smoked. DESIGN: This randomized controlled cross-over trial had two 14-day intervention periods with an intervening 7-day period of usual behaviour. Participants purchased their own cigarettes. They were instructed to smoke their usual brand from either one of two sizes of pack in each of two 14-day intervention periods: (a) 25 cigarettes and (b) 20 cigarettes. Participants were randomized to the order in which they smoked from the two pack sizes (a-b; b-a). SETTING: Canada. PARTICIPANTS: Participants were adult smokers who smoked from pack sizes of 25, recruited between July 2020 and June 2021. Of 252 randomized, 240 (95%) completed the study and 236 (94%) provided sufficient data for the primary analysis. MEASUREMENTS: Cigarettes smoked per participant per day. FINDINGS: Participants smoked fewer cigarettes per day from packs of 20 cigarettes [n = 234, mean = 15.7 standard deviation (SD) = 7.1] than from packs of 25 (n = 235, mean = 16.9, SD = 7.1). After adjusting for pre-specified covariates (baseline consumption and heaviness of smoking), modelling estimated that participants smoked 1.3 fewer cigarettes per day [95% confidence interval (CI) = -1.7 to -0.9], equivalent to 7.6% fewer (95% CI = -10.1 to -5.2%) from packs of 20 cigarettes. CONCLUSIONS: Smoking from packs of 20 compared with 25 cigarettes reduced the number of cigarettes smoked per day.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".