The Effect of Alcohol Cue Exposure on Tobacco-Related Cue Reactivity: A Systematic Review
Bibliographic record
Abstract
AIMS: To examine the effect of alcohol cue exposure on tobacco-related cravings, self-administration and other measures of tobacco-related cue reactivity. METHODS: We searched Medline, PsycINFO, Embase, CINAHL and Scopus from inception to May 2020 for articles reporting on a combination of cue reactivity (and/or cross-cue reactivity), alcohol use and tobacco consumption. A semi-quantitative analysis and study quality assessment were performed for the included articles. RESULTS: A total of 37 articles met our inclusion criteria and were included in the systematic review. Most studies (60%) reported that alcohol cue exposure increased tobacco cravings, but only 18% of studies reported that alcohol cue exposure resulted in an increase in ad libitum smoking. There was also substantial heterogeneity between studies due to differences in methodology related to alcohol cue exposure, measures of tobacco cravings, as well as variable participant and study characteristics. CONCLUSIONS: Alcohol cue exposure can increase cravings for tobacco. This has important implications for individuals who use both substances but are trying to quit one or both.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".