Reducing nicotine exposure results in weight gain in smokers randomised to very low nicotine content cigarettes
Bibliographic record
Abstract
BACKGROUND: The Food and Drug Administration can reduce the nicotine content in cigarettes to very low levels. This potential regulatory action is hypothesised to improve public health by reducing smoking, but may have unintended consequences related to weight gain. METHODS: Weight gain was evaluated from a double-blind, parallel, randomised clinical trial of 839 participants assigned to smoke 1 of 6 investigational cigarettes with nicotine content ranging from 0.4 to 15.8 mg/g or their own usual brand for 6 weeks. Additional analyses evaluated weight gain in the lowest nicotine content cigarette groups (0.4 and 0.4 mg/g, high tar) to examine the effect of study product in compliant participants as assessed by urinary biomarkers. Differences in outcomes due to gender were also explored. FINDINGS: There were no significant differences in weight gain when comparing the reduced nicotine conditions with the 15.8 mg/g control group across all treatment groups and weeks. However, weight gain at week 6 was negatively correlated with nicotine exposure in the 2 lowest nicotine content cigarette conditions. Within the 2 lowest nicotine content cigarette conditions, male and female smokers biochemically verified to be compliant on study product gained significantly more weight than non-compliant smokers and control groups. CONCLUSIONS: The effect of random assignment to investigational cigarettes with reduced nicotine on weight gain was likely obscured by non-compliance with study product. Men and women who were compliant in the lowest nicotine content cigarette conditions gained 1.2 kg over 6 weeks, indicating weight gain is a likely consequence of reduced exposure to nicotine. TRIAL REGISTRATION NUMBER: NCT01681875, Post-results.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".