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Record W4395081252 · doi:10.1097/ta.0000000000004344

Characteristics of firearm injury by injury intent: The need for tailored interventions

2024· article· en· W4395081252 on OpenAlexaff
Shelbie D. Waddle, Ashley Hink, Deborah A. Kuhls, Frederick P. Rivara, Joseph V. Sakran, Lauren L. Agoubi, Alex Winchester, Jacy Richards, Christopher Hoeft, Bhavin Patel, Holly Michaels, Avery B. Nathens

Bibliographic record

VenueThe Journal of Trauma: Injury, Infection, and Critical Care · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGun Ownership and Violence Research
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersUniversity of Florida HealthWest Virginia UniversityVirginia Commonwealth UniversityUniversity of LouisvilleUniversity of MissouriTexas Children's HospitalChildren's Hospital of PhiladelphiaYale University
KeywordsPsychological interventionMedical emergencyInjury preventionMedicinePoison controlForensic engineeringEngineeringNursing

Abstract

fetched live from OpenAlex

INTRODUCTION: While the United States has high quality data on firearm-related deaths, less information is available on those who arrive at trauma centers alive, especially those discharged from the emergency department. This study sought to describe characteristics of patients arriving to trauma centers alive following a firearm injury, postulating that significant differences in firearm injury intent might provide insights into injury prevention strategies. METHODS: This was a multicenter prospective cohort study of patients treated for firearm-related injuries at 128 US trauma centers from March 2021 to February 2022. Data collected included patient-level sociodemographic, injury and clinical characteristics, community characteristics, and context of injury. The outcome of interest was the association between these factors and the intent of firearm injury. Measures of urbanicity, community distress, and strength of state firearm laws were used to characterize patient communities. RESULTS: A total of 15,232 patients presented with firearm-related injuries across 128 centers in 41 states. Overall, 9.5% of patients died, and deaths were more common among law enforcement and self-inflicted firearm injuries (80.9% and 50.5%, respectively). These patients were also more likely to have a history of mental illness. Self-inflicted firearm injuries were more common in older White men from rural and less distressed communities, whereas firearm assaults were more common in younger Black men from urban and more distressed communities. Unintentional injuries were more common among younger patients and in states with lower firearm safety grades, whereas law enforcement-related injuries occurred most often in unemployed patients with a history of mental illness. CONCLUSION: Injury, clinical, sociodemographic, and community characteristics among patients injured by a firearm significantly differed between intents. With the goal of reducing firearm-related deaths, strategies and interventions need to be tailored to include community improvement and services that address specific patient risk factors for firearm injury intent. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level III.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.418
Teacher spread0.368 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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