The Effectiveness of Nicotine Replacement Therapy in Light Versus Heavier Smokers
Bibliographic record
Abstract
INTRODUCTION: The prevalence of light smoking has increased in North America; however, research on the effectiveness of current treatments in this subpopulation of smokers is limited. We compared quit outcomes between light (1-10 cigarettes per day [CPD]) versus heavier smokers (>10 CPD) enrolled in a treatment program at their primary care clinic. AIMS AND METHODS: This secondary analysis analyzed 45 087 participants (light smokers [n = 9861]; heavier smokers [n = 35 226]) enrolled in a smoking cessation program between April 2016 and March 2020. The program offered cost-free nicotine replacement therapy (NRT) plus in-person counseling. Type, dose, and duration of NRT treatment were personalized. Data were collected at baseline, and at 6 months following enrollment to assess 7-day point prevalence abstinence (PPA), the primary outcome variable of interest. Logistic regression models were used for analyses. RESULTS: Seven-day PPA at 6 months was significantly higher among light smokers (30.6%) than heavier smokers (26.0%; odds ratio = 1.25, 95% confidence interval = 1.18-1.33, p < .001). Heavier smokers were prescribed more weeks of NRT than light smokers (B = 0.82, 95% confidence interval = 0.64-1.0, p < .001). The association between smoking cessation and daily NRT dose did not differ between groups (p = .98). However, a stronger positive relationship between the number of clinic visits attended and 7-day PPA was found among heavier smokers in comparison to light smokers (p < .001). All findings remained significant after adjusting for baseline variables. CONCLUSIONS: There is a paucity of scientific literature on the effectiveness of NRT for light smokers. Our findings suggest that individualized doses of NRT may be helpful in these subpopulations, and highlight the different treatment needs of light smokers. IMPLICATIONS: Current clinical guidelines do not provide formal recommendations for light smokers who want to quit smoking. Similar to heavy smokers, light smokers are at substantial risk for many adverse health problems. As such, it is important to understand what treatment options are effective in assisting light smokers to quit smoking. Findings from this study support the use of personalized treatment for all smokers who are interested in quitting smoking, including light smokers.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".