Alcohol consumption on alcohol mixed with energy drink (AMED) occasions versus alcohol only occasions: A meta-analysis of within-subject studies
Bibliographic record
Abstract
Purpose: It has been suggested that mixing alcohol with energy drink increases overall alcohol consumption. The aim of this study was to determine if mixing alcohol with energy drink (AMED) has an impact on overall alcohol consumption through examining data from across the world.\nMethods: A literature search (PubMed, Embase, PsycINFO) was conducted to identify studies applying a within-subject comparison among AMED consumers comparing alcohol consumption on occasions when they consume AMED versus alcohol only occasions. The data were combined into a meta-analysis.\nResults: The literature search identified five studies (Price et al. 2010, Woolsey et al. 2010, Brache and Stockwell 2011, De Haan et al. 2012, Peacock et al. 2012), including N=1814 AMED consumers. The meta-analysis revealed no significant difference in overall alcohol consumption between AMED and alcohol only occasions (differences across the 5 studies; p=0.669, 95%CI: 0.183 to 0.285).\nConclusion: No significant difference in overall alcohol consumption was observed between AMED and alcohol only occasions. Interestingly, the contributing studies were drawn from research in Canada, the USA, Australia and the Netherlands. Earlier research suggested possible differences in alcohol consumption between AMED and alcohol only groups. These overall findings with 1800 participants drawn from 3 continents suggest that there is no modification of alcohol consumption through the combined consumption of energy drink and alcohol when compared to alcohol alone when investigating the same participants on both drinking occasions.\n
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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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.063 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".