Epidemiology of Gunshot Wounds to the Nervous System Before and After the COVID-19 Pandemic: A 14-Year Single-Institution Retrospective Review
Bibliographic record
Abstract
Firearm injuries continue to be an enduring and important United States public health issue, of which injuries to the nervous system are particularly disabling. The social dislocation caused by the COVID-19 pandemic has exacerbated this ongoing issue. Our group hypothesized that COVID-19 may have increased ballistic incidents and that this might have affected certain demographics more than others. The University of Mississippi Medical Center neurotrauma registry was searched for any gunshot wounds (GSWs) involving the nervous system and its coverings from Q1 2009 to Q2 2024. We defined Q2 2020 as the period when the COVID-19 pandemic began in the state of Mississippi. Our study at the state's only level I trauma center demonstrated that GSWs increased significantly from pre- to post-COVID-19, from an average of 16.80 GSWs per quarter pre-COVID-19 to 39.00 GSWs per quarter post-COVID-19. Adult GSWs accounted for 15.33 GSWs per quarter pre-COVID-19 and 31.56 GSWs per quarter post-COVID-19. Pediatric GSWs accounted for 1.42 GSWs per quarter pre-COVID-19 and 7.44 GSWs per quarter post-COVID-19. The increase was significant in both groups. The rate of change in GSWs per quarter, both pre- and post-COVID-19, was not significantly different in either group. The age distribution of pediatric GSWs demonstrated a similar bimodal distribution pre- and post-COVID-19, with approximately 2/3 of pediatric GSWs affecting those between 14 and 17 years of age. Our study reflects national trends in the distribution of GSWs and corroborates the finding that there has been a stepwise increase in pediatric GSWs since the onset of the COVID-19 pandemic. Our study suggests a need for increased attention to preventing gun-related injuries and demarcation of older adolescents (ages 14-17) as a high-risk group.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".