Acute alcohol use and the risk of non‐fatal injury in sixteen countries
Bibliographic record
Abstract
AIMS: To determine the relative risk (RR) of non-fatal injury associated with alcohol consumption in a series of emergency departments (EDs), possible effect modifiers and the impact of contextual variables on differences across sites. DESIGN: The case-crossover method was used to obtain RR estimates of the effect of alcohol on non-fatal injuries. Meta-analysis was used to evaluate the consistency and magnitude of RR across sites, and the extent to which contextual variables explain differences in effect sizes. PARTICIPANTS: Probability samples of 11,536 injured patients attending 28 EDs studies in 16 countries (1984-2002). The majority of the sample was male (65%) and > 30 years old (53%). MEASUREMENTS: Exposed cases where those that consumed alcohol 6 hours prior to the injury. Usual alcohol consumption served as the control period. FINDINGS: Drinking within 6 hours prior to the injury was reported by 21% of the sample. The estimated (random) pooled relative risk for patients who reported alcohol use within 6 hours prior to injury was 5.69 (95% confidence interval = 4.04-8.00), ranging from 1.05 in Canada to 35.00 in South Africa. Effect size was not homogeneous across studies, as societies with riskier consumption patterns had a higher relative risk for injury. Heavier drinkers also showed lower RR. CONCLUSIONS: Acute alcohol was a risk factor for non-fatal injuries in most sites. Policy measures addressed to the general population are recommended, especially in societies with riskier consumption patterns.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.011 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".