Prevalence of Alcohol and Other Drug Use in Patients Presenting to Hospital for Violence-Related Injuries: A Systematic Review
Bibliographic record
Abstract
Substance use is a risk factor for being both a perpetrator and a victim of violence. The aim of this systematic review was to report the prevalence of acute pre-injury substance use in patients with violence-related injuries. Systematic searches were used to identify observational studies that included patients aged ≥15 years presenting to hospital after violence-related injuries and used objective toxicology measures to report prevalence of acute pre-injury substance use. Studies were grouped based on injury cause (any violence-related, assault, firearm, and other penetrating injuries including stab and incised wounds) and substance type (any substance, alcohol only, drugs other than alcohol only), and they were summarized using narrative synthesis and meta-analyses. This review included 28 studies. Alcohol was detected in 13%–66% of any violence-related injuries (five studies), 4%–71% of assaults (13 studies), 21%–45% of firearm injuries (six studies; pooled estimate = 41%, 95% CI: 40%–42%, n = 9,190), and 9%–66% of other penetrating injuries (nine studies; pooled estimate = 60%, 95% CI: 56%–64%, n = 6,950). Drugs other than alcohol were detected in 37% of any violence-related injuries (one study), 39% of firearm injuries (one study), 7%–49% of assaults (five studies), and 5%–66% of penetrating injuries (three studies). The prevalence of any substance varied across injury categories: any violence-related injuries = 76%–77% (three studies), assaults = 40%–73% (six studies), firearms = n/a, other penetrating injuries = 26%–45% (four studies; pooled estimate = 30%, 95% CI: 24%–37%, n = 319).Overall, substance use was frequently detected in patients presenting to hospital for violence-related injuries. Quantification of substance use in violence-related injuries provides a benchmark for harm reduction and injury prevention strategies.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".