Alcohol and Drug Use as Predictors of Intentional Injuries in Two Emergency Departments in British Columbia
Bibliographic record
Abstract
BACKGROUND: While a substantial literature exists demonstrating a strong association of alcohol and intentional injury, less is known about the association of intentional injury with recreational drug use, either alone, or in combination with alcohol. OBJECTIVES: The risk of intentional injury due to alcohol and other drug use prior to injury is analyzed in a sample of emergency department (ED) patients. METHODS: Logistic regression was used to examine the predictive value of alcohol and drug use on intentional versus non-intentional injury in a probability sample of ED patients in Vancouver, BC (n = 436). RESULTS: Those reporting only alcohol use were close to four times more likely (OR = 3.73) to report an intentional injury, and those reporting alcohol combined with other drug(s) almost 18 times more likely (OR = 17.75) than those reporting no substance use. Those reporting both alcohol and drug use reported drinking significantly more alcohol (15.7 drinks) than those reporting alcohol use alone (5 drinks). CONCLUSIONS: These data suggest that alcohol in combination with other drugs may be more strongly associated with intentional injury than alcohol alone. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: The strong association of alcohol combined with other drug use on injury may be due to the increased amount of alcohol consumed by those using both substances, and is an area requiring more research with larger samples of intentional injury patients.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".