Evaluating Treatment Mechanisms of Varenicline: Mediation by Affect and Craving
Bibliographic record
Abstract
INTRODUCTION: Negative reinforcement models posit that relapse to cigarette smoking is driven in part by changes in affect and craving during the quit attempt. Varenicline may aid cessation by attenuating these changes; however, this mediational pathway has not been formally evaluated in placebo-controlled trials. Thus, trajectories of negative affect (NA), positive affect (PA), and craving were tested as mediators of the effect of varenicline on smoking cessation. AIMS AND METHODS: Secondary data analysis was conducted on 828 adults assigned to either varenicline or placebo in a randomized controlled trial for smoking cessation (NCT01314001). Self-reported NA, PA, and craving were assessed 1-week pre-quit, on the target quit day (TQD), and 1 and 4 weeks post-TQD. RESULTS: Across time, NA peaked 1-week post-quit, PA did not change, and craving declined. Less steep rises in NA (indirect effect 95% CI: .01 to .30) and lower mean craving at 1-week post-quit (CI: .06 to .50) were mediators of the relationship between varenicline and higher cessation rates at the end of treatment. PA was associated with cessation but was not a significant mediator. CONCLUSIONS: These results partially support the hypothesis that varenicline improves smoking cessation rates by attenuating changes in specific psychological processes and supported NA and craving as plausible treatment mechanisms of varenicline. IMPLICATIONS: The present research provides the first evidence from a placebo-controlled randomized clinical trial that varenicline's efficacy is due, in part, to post-quit attenuation of NA and craving. Reducing NA across the quit attempt and craving early into the attempt may be important treatment mechanisms for effective interventions. Furthermore, post-quit NA, PA, and craving were all associated with relapse and represent treatment targets for future intervention development.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".