Gender differences in the consumption of alcohol mixed with caffeine and risk of injury
Bibliographic record
Abstract
INTRODUCTION AND AIMS: There is increasing evidence suggesting the consumption of caffeinated alcoholic beverages is associated with risks over and above alcohol use on its own; however, research in this area remains limited. We examined whether gender differences existed in the relationship between the combined use of alcohol and caffeine (Alc + Caff) and risk for injury. DESIGN AND METHODS: This emergency department study utilised case-control and case-crossover analyses to examine in situ session specific Alc + Caff use and injury risk for men and women, while controlling for socio-demographic variables, dose of alcohol and caffeine, other substance use, risk-taking propensity and context. The sample comprised 2804 individuals aged 18-years or older who presented to three hospital emergency departments in British Columbia. RESULTS: A relationship between Alc + Caff use and increased risk of injury was confirmed. Further, gender differences were found in the risk relationship between Alc + Caff use and injury. Women were found to have a higher risk injury propensity following Alc + Caff use in both the case-control (OR = 3.10, 95% CI = 1.78, 5.84) and case-crossover analyses (OR = 3.21, 95% CI = 1.69, 6.12), relative to men (OR = 1.69, 95% CI = 1.30, 2.30; OR = 1.38, 95% CI = 1.08, 1.86). These results remained even after controlling for demographic factors, risk-taking, context and other substance use. DISCUSSION AND CONCLUSIONS: Women may be at higher risk of injury than men following the consumption of alcohol mixed with caffeine. The findings offer support for differential low-risk drinking guidelines for men and women and the restriction and regulation of the sale and availability of caffeinated alcoholic beverages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".