Impact of the COVID-19 pandemic on gunshot injuries at a level-1 trauma centre: a retrospective study on a 5-year period
Bibliographic record
Abstract
<h3>Background:</h3> Gunshot injuries are a major cause of morbidity and mortality, and evidence shows that violent crimes increased during the COVID-19 pandemic. The aim of this study was to investigate the impact of the pandemic on the prevalence of gunshot injuries and to analyze the demographic characteristics of patients with gunshot injury at a level-1 trauma centre. <h3>Methods:</h3> We conducted a retrospective analysis from April 2018 to February 2023. We collected demographic information, injury type, weapon involved, and mechanism of injury. We examined the annual incidence of gunshot injuries to assess the potential influence of COVID-19-related public health measures on rates of violent injury. <h3>Results:</h3> We identified 158 patients with gunshot injury. The mean age of patients was 35 (range 18 to 78) years, and 9% were women. Seventy percent were homicide attempts, 8% were suicide attempts, and 20% were unspecified. Weapons used included low-velocity handguns (78%) and hunting rifles (7%), and the remainder were unspecified. There were no injuries from military or other high-velocity firearms. Emergency department patients with hemodynamic shock (18%) were 7.5 times more likely to die before discharge than stable patients (29% v. 4%). Gunshot injuries significantly increased by 52% during the COVID-19 period compared with the baseline period (<i>p</i> = 0.03). After the COVID-19 period, injuries significantly decreased (<i>p</i> = 0.048), returning to levels statistically indistinguishable from the baseline period (<i>p</i> = 0.7). Seasonal variation analysis confirmed significant peaks during the summer and early autumn months. <h3>Conclusion:</h3> This study highlights the impact of the COVID-19 pandemic on gun violence, with a significant increase in the number of firearm injury victims during this period. Our findings show a return to prepandemic baseline levels in 2022.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".