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Record W2329849910 · doi:10.1037/a0038250

Alcohol and tobacco cue effects on craving in non-daily smokers.

2014· article· en· W2329849910 on OpenAlexafffund
Marcel P. J. Peloquin, Daniel S. McGrath, Dessislava Telbis, Sean P. Barrett

Bibliographic record

VenueExperimental and Clinical Psychopharmacology · 2014
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCravingAlcoholCLIPSNicotineAlcohol consumptionPlaceboMedicineAudiologyPsychologyPhysiologyInternal medicinePsychiatrySurgeryAddiction

Abstract

fetched live from OpenAlex

Non-daily smokers commonly smoke cigarettes following the consumption of alcohol, yet the reason(s) for this remains poorly understood. The present study examined the impact of alcohol consumption on responses in tobacco salient cues 49 male and 50 female non-daily smokers. After the administration of an alcohol, placebo, or control beverage, participants were exposed to series neutral video clips and tobacco smoking salient video clips, and their subjective states and heart rates were monitored. The timing of the exposure to the tobacco smoking clips was randomly determined to coincide with the timing of either the ascending limb or the descending limb of the blood alcohol concentration (BAC) curve of the alcohol beverage condition. The tobacco smoking clips were found to increase cigarette craving regardless of beverage condition or timing of exposure (p = .002). Alcohol consumption was associated with increased ratings of intoxication (p < .001), increased heart rate across participants (p < .001), and increased cigarette craving in female participants specifically (p = .017). Alcohol did not influence responses to the smoking videos. These results suggest that smoking salient cues and alcohol may impact cigarette craving in non-daily smokers through independent processes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.429
Teacher spread0.398 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations7
Published2014
Admission routes2
Has abstractyes

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