A Systematic Review and Meta‐Analysis of Alcohol Consumption and Injury Risk as a Function of Study Design and Recall Period
Bibliographic record
Abstract
BACKGROUND: It is well established that alcohol consumption is associated with an increased risk of injury. This systematic review and meta-analysis addresses important methodological issues commonly encountered in the alcohol and injury field by delineating the effect of study design and alcohol consumption recall period on effect size magnitude and by conducting gender-specific analyses. METHODS: We performed meta-analyses using random-effect models. Data sources were peer-reviewed studies on alcohol and injury from 1970 to 2009 from MEDLINE, PsychInfo, and on-line journals. Case-control or case-crossover emergency department (ED) studies reporting injury risk from alcohol consumption 6 hours before injury were included. RESULTS: The overall odds of injury were 2.799 (2.214 to 3.538, p < 0.001). For case-crossover studies, the odds were 3.815 (2.646 to 5.499, p < 0.001); for ED case-control studies, the odds were 1.977 (1.385 to 2.821, p < 0.001); and for population case-control designs, the odds were 3.145 (1.583 to 6.247, p < 0.005). The "usual frequency" recall period yielded an odds ratio of 4.235 (2.541 to 7.057, p < 0.001), compared to 2.320 (1.789 to 3.008, p < 0.001) for all other methods. There were significant differences in odds ratio magnitude when comparing studies by design and recall period. Females had higher odds of injury than males, 2.285 (1.361 to 3.836, p < 0.005) versus 1.071 (0.715 to 1.605, p = 0.737). CONCLUSIONS: Study design and alcohol consumption recall period have significant effects on effect size magnitude in estimating the risk of injury from alcohol consumption 6 hours prior to injury. For the "usual frequency" case-crossover design, significant moderator effects were found, resulting in overestimates of injury risk from alcohol. ED case-crossover designs tend to overestimate risk, and ED case-control designs tend to underestimate. We provide recommendations for future ED research.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".