Alcohol mixed with energy drink (AMED): A critical review and meta‐analysis
Bibliographic record
Abstract
The purpose of this systematic review and meta-analysis was to critically review the (1) prevalence of alcohol mixed with energy drink (AMED) consumption, (2) motives for AMED consumption, (3) correlates of AMED consumption, and (4) whether AMED consumption has an impact on (a) alcohol consumption, (b) subjective intoxication, and (c) risk-taking behavior. Overall a minority of the population consumes AMED, typically infrequently. Motives for AMED consumption are predominantly hedonistic and social. Meta-analyses revealed that AMED consumers drink significantly more alcohol than alcohol-only (AO) consumers. Within-subject comparisons restricted to AMED consumers revealed that alcohol consumption does not significantly differ between typical AMED and AO occasions. On past month heaviest drinking occasions, AMED users consume significantly less alcohol on AMED occasions when compared to AO occasions. AMED consumers experience significantly fewer negative consequences and risk-taking behavior on AMED occasions compared with AO occasions. Meta-analyses of subjective intoxication studies suggest that AMED consumption does not differentially affect subjective intoxication when compared to AO consumption. In conclusion, when compared to AO consumption, mixing alcohol with energy drink does not affect subjective intoxication and seems unlikely to increase total alcohol consumption, associated risk-taking behavior, nor other negative alcohol-related consequences. Further research may be necessary to fully reveal the effects of AMED.
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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 | 0.019 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.007 | 0.006 |
| 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) | 0.005 | 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".