Risk of injury from alcohol and drug use in the emergency department: A case‐crossover study
Bibliographic record
Abstract
INTRODUCTION AND AIMS: A substantial literature exists demonstrating the risk of injury from alcohol, but less is known about the association of alcohol in combination with other drugs and injury. This study examined the risk of injury associated with alcohol and drug use prior to the event. DESIGN AND METHODS: Case-crossover analysis was used to estimate the relative risk (RR) of injury due to alcohol use alone, compared with alcohol in combination with other drug use in a sample of emergency department injured patients from two sites in Vancouver, British Columbia (n = 443). Alcohol and drug use in the 6 h prior to injury was compared with the patient's use of these substances during the same 6 h period the day prior and the week prior to injury. RESULTS: Using multiple matching for the two control time periods, RR of injury was significantly related to both alcohol use (RR = 3.3) and to alcohol combined with drug use (RR = 3.0), but not to drug use alone. Effect modification was found only for age for alcohol combined with drug use, with a significant increase in injury risk (P = 0.087) for those over 30. DISCUSSION AND CONCLUSION: While a similar elevated risk of injury was found for alcohol use alone and alcohol used with other drugs, the literature suggests that alcohol in combination with some drugs may be potentially more risky for injury occurrence. Findings suggest the need for future research on risk of injury for specific alcohol and drug combinations.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".