Do lab-based assessments of pretreatment smoking reinforcement and cue-specific craving predict smoking cessation with varenicline?
Bibliographic record
Abstract
OBJECTIVE: Individual differences in smoking reinforcement and cue-specific cigarette craving are theorized to influence smoking cessation and relapse. However, there has been little laboratory research that prospectively evaluates these relationships. The present study evaluated whether lab-based indices of pretreatment smoking reinforcement and cue-specific craving predicted subsequent bio-verified abstinence. METHOD: Participants were 253 adults (aged 28-70, 54% female, 78% White, 3% Hispanic) who reported smoking more than five cigarettes per day when enrolled in a randomized, double-blind, placebo-controlled smoking cessation trial (NCT03262662). During a lab visit ∼1 week before treatment began, participants completed the Choice Behavior under Cued Conditions task. On each of the 36 Choice Behavior under Cued Conditions trials, participants spent between $0.01 and $0.25 for a chance (5%-95%) to sample a cigarette or a cup of water. All participants received varenicline, either during Weeks 1-15 or 4-15 of the study, along with counseling at each visit, and attempted to quit smoking at the end of Week 4. Cotinine-bio-verified (< 15 ng/mL) 7-day point-prevalence abstinence was assessed at Weeks 6, 8, 15, and 28. The predictive validity of pretreatment smoking reinforcement and cue-specific craving on abstinence were examined in logistic regressions. RESULTS: As predicted, greater pretreatment smoking reinforcement predicted lower odds of abstinence, an effect that did not vary significantly across time, treatment groups, or biological sex. Pretreatment cue-specific craving was not predictive of abstinence. CONCLUSIONS: This study highlights the theoretical importance of smoking reinforcement and the predictive utility of Choice Behavior under Cued Conditions in identifying those at risk for relapse. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".