Gunshot Injuries: A Review of Demographics and Extremity Injuries in Patients Treated at Harlem Hospital, New York City
Bibliographic record
Abstract
Background: Gunshot wounds (GSWs) remain a significant public health problem in the United States, particularly in urban communities. Extremity injuries account for a substantial proportion of firearm-related trauma, often resulting in considerable morbidity and resource utilization. Objective: To analyze the demographics, injury patterns, and clinical outcomes of patients with extremity GSWs treated at Harlem Hospital from 2015 to 2018. Methods: A retrospective review was conducted using the Harlem Hospital trauma registry. Data collected included patient demographics, mechanism and location of injury, Injury Severity Score (ISS), Glasgow Coma Scale (GCS) on arrival, associated injuries, hospital disposition, and mortality. Results: A total of 96 patients were identified, predominantly male (94.8%) with a mean age of 27.9 years. Lower extremity injuries, including gluteal wounds, accounted for 69.8% of cases, while upper extremity injuries comprised 30.2%. The majority sustained soft tissue injuries (60.4%), followed by fractures (34.3%), major vascular injuries (4.1%), and severe nerve injuries (1%). Most patients (78.1%) were admitted, primarily to the surgical floor, with 8.3% requiring immediate operative intervention. Three patients (3.1%) died on arrival, all from major vascular injuries. The mean ISS was 3.75, and the mean GCS was 14.5. Conclusions: Extremity GSWs in the Harlem community predominantly affect young males and most commonly involve the lower extremities. Although mortality is low, a substantial proportion of patients require hospital admission and surgical intervention. These findings highlight the ongoing burden of firearm-related extremity trauma and underscore the need for targeted prevention and violence reduction strategies in urban settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".