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Record W3133329630 · doi:10.1093/alcalc/agab009

The Effect of Alcohol Cue Exposure on Tobacco-Related Cue Reactivity: A Systematic Review

2021· review· en· W3133329630 on OpenAlexaff
Emma V. Ritchie, Chelsea L. Fitzpatrick, Paul E. Ronksley, Alexander A. C. Leung, Sydney Seidel, Daniel S. McGrath

Bibliographic record

VenueAlcohol and Alcoholism · 2021
Typereview
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsHealth Sciences CentreYork UniversityUniversity of Calgary
Fundersnot available
KeywordsCue reactivityCINAHLPsycINFOAlcoholReactivity (psychology)Meta-analysisPsychologyCravingSmoking cessationMedicineClinical psychologyEnvironmental healthMEDLINEPsychiatryAddictionPsychological interventionInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.316
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.359
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations8
Published2021
Admission routes1
Has abstractyes

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