A decade of firearm injuries: Have we improved?
Bibliographic record
Abstract
BACKGROUND: Firearm injuries are a growing public health issue, with marked increases coinciding with the coronavirus disease 2019 (COVID-19) pandemic. This study evaluates temporal trends over the past decade, hypothesizing that despite a growing number of injuries, mortality would be unaffected. In addition, the study characterizes the types of centers affected disproportionately by the reported firearm injury surge in 2020. METHODS: Patients 18 years and older with firearm injuries from 2011 to 2020 were identified retrospectively using the National Trauma Data Bank (NTDB®). Trauma centers not operating for the entirety of the study period were excluded to allow for temporal comparisons. Joinpoint regression and risk-standardized mortality ratios (SMR) were used to evaluate injury counts and adjusted mortality over time. Subgroup analysis was performed to describe centers with the largest increases in firearm injuries in 2020. RESULTS: A total of 238,674 patients, treated at 420 unique trauma centers, met inclusion criteria. Firearm injuries increased by 31.1% in 2020, compared to an annual percent change of 2.4% from 2011 to 2019 ( p = 0.01). Subset analysis of centers with the largest changes in firearm injuries in 2020 found that they were more often Level I centers, with higher historic trauma volumes and percentages of firearm injuries ( p < 0.001). Unadjusted mortality decreased by 0.9% from 2011 to 2020, but after controlling for demographics, injury characteristics and physiology, there was no difference in adjusted mortality over the same time period. However, among patients with injury severity scores ≥25, adjusted mortality improved compared with 2011 (SMR of 0.950 in 2020; 95% confidence interval, 0.916-0.986). CONCLUSION: Firearm injuries pose an increasing burden to trauma systems, with Level I and high-volume centers seeing the largest growth in 2020. Despite increasing numbers of firearm injuries, mortality has remained unchanged over the past decade. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level III.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".