A Geospatial Analysis of Severe Firearm Injuries Compared to Other Injury Mechanisms: Event Characteristics, Location, Timing, and Outcomes
Bibliographic record
Abstract
OBJECTIVES: Relatively little is known about the context and location of firearm injury events. Using a prospective cohort of trauma patients, we describe and compare severe firearm injury events to other violent and nonviolent injury mechanisms regarding incident location, proximity to home, time of day, spatial clustering, and outcomes. METHODS: This was a secondary analysis of a prospective cohort of injured children and adults with hypotension or Glasgow Coma Scale score ≤ 8, injured by one of four primary injury mechanisms (firearm, stabbing, assault, and motor vehicle collision [MVC]) who were transported by emergency medical services to a Level I or II trauma center in 10 regions of the United States and Canada from January 1, 2010, through June 30, 2011. We used descriptive statistics and geospatial analyses to compare the injury groups, distance from home, outcomes, and spatial clustering. RESULTS: There were 2,079 persons available for analysis, including 506 (24.3%) firearm injuries, 297 (14.3%) stabbings, 339 (16.3%) assaults, and 950 (45.7%) MVCs. Firearm injuries resulted in the highest proportion of serious injuries (66.3%), early critical resources (75.3%), and in-hospital mortality (53.5%). Injury events occurring within 1 mile of a patient's home included 53.9% of stabbings, 49.2% of firearm events, 41.3% of assaults, and 20.0% of MVCs; the non-MVC events frequently occurred at home. While there was geospatial clustering, 94.4% of firearm events occurred outside of geographic clusters. CONCLUSIONS: Severe firearm events tend to occur within a patient's own neighborhood, often at home, and generally outside of geospatial clusters. Public health efforts should focus on the home in all types of neighborhoods to reduce firearm violence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".