Firearm injuries treated at trauma centers in the United States
Bibliographic record
Abstract
BACKGROUND: While firearm injuries and deaths continue to be a major public health problem, the number of nonfatal firearm injuries and the characteristics of patients are not well-known. The American College of Surgeons Committee on Trauma leveraged an existing data system to collect additional data on fatal and nonfatal firearm injuries presenting to trauma centers. This report provides an overview of this initiative and highlights the challenges associated with capturing actionable data on firearm-injured patients. METHODS: A total of 128 trauma centers that are part of the American College of Surgeons Trauma Quality Improvement Program collected data on individuals of any age arriving alive between March 1, 2021, and February 28, 2022, with a firearm injury. In addition to the standard data collected for Trauma Quality Improvement Program, abstractors also extracted additional data specific to this study. We linked data from the Distressed Community Index to patient records using zip code of residence. RESULTS: A total of 17,395 patients were included, with mean (SD) age of 30.2 (13.5) years, 82.5% were male, and the majority were Black and non-Hispanic. The mean proportion of variables with missing data varied among trauma centers, with a mean of 20.7% missing data. Injuries occurred most commonly in homes (31.2%) or on the street (26.6%); 70.4% of injuries were due to assaults. Nearly one third of patients were discharged from the emergency department, 25.9% were admitted directly to the operating room, and 10.9% were admitted to the intensive care unit; 5.9% died in the emergency department, and 10.3% died overall during their course of care. Nearly two thirds of patients lived in the two highest distressed categories of communities; only 7.5% lived in the least distressed quintile. CONCLUSION: Using trauma center data can be a valuable tool to improve our knowledge of firearm injuries if clinical practices and documentation of patient risks and circumstances are standardized. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level III.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".