Prevalence of Alcohol and Other Drug Use in Patients Presenting to Hospital for Violence-Related Injuries: A Systematic Review
Why this work is in the frame
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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%, <i>n</i> = 9,190), and 9%–66% of other penetrating injuries (nine studies; pooled estimate = 60%, 95% CI: 56%–64%, <i>n</i> = 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%, <i>n</i> = 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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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it