Impact of nicotine reduction in cigarettes on smoking behavior and exposure: Are there differences by race/ethnicity, educational attainment, or gender?
Bibliographic record
Abstract
Background: Lowering nicotine in cigarettes may reduce smoking prevalences; however, it is not known whether an immediate or gradual reduction in nicotine is the optimal approach for all population groups. Objectives: We examined whether the optimal approach to nicotine reduction depended on the education, gender, or race of people who smoke and whether the optimal approach differentially benefited people who smoke based on their education, gender, or race. Methods: Secondary analysis was conducted on a randomized clinical trial (N = 1250) comparing (1) immediate reduction from 15.5 to 0.4 mg of nicotine per gram of tobacco (mg/g);(2) gradual reduction to 0.4 mg/g;(3) control group with normal nicotine cigarettes(15.5 mg/g). Outcomes included cigarettes per day(CPD), carbon monoxide(CO), total nicotine equivalents(TNE), 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol and its glucuronides(NNAL), phenanthrene tetraol(PheT), N-Acetyl-S-(2-cyanoethyl)-L-cysteine(CEMA). Data were analyzed as area under the curve(AUC). Results: Results were presented by education (High school [HS] or less n = 505, more than HS n = 745), gender (males n = 701, females n = 549), and race (Black participants n = 373,White participants n = 758). Regardless of education, gender, and race, CPD, CO, TNE, NNAL, PheT, and CEMA were lower in immediate versus gradual nicotine reduction. Comparing immediate versus the control, outcomes were lower for all subgroups; however, the magnitude of the effect for TNE varied by race. Specifically, geometric mean of the AUC of TNE in immediate versus gradual was 49 % lower in Black participants and 61 % lower in White participants (p-value = 0.047). Conclusions: Immediately reducing nicotine in cigarettes has the potential to benefit people who smoke across lower and higher educational attainment, male and female gender, and Black and White race.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".