Nerve Injury Related to Firearm Extremity Trauma
Bibliographic record
Abstract
Introduction: Firearm-related extremity trauma with nerve injury can lead to life-altering impairment and disability. This study evaluated the frequency of nerve injury in firearm-related extremity injuries at a Level 1 trauma centre, and the rate of nerve transection in firearm-related peripheral nerve injuries (PNIs) and brachial plexus injuries (BPIs). Methods: Following Ethics Board approval, institutional trauma and emergency databases (from 2000 to 2020) were used to identify adults with firearm-related PNI or BPI treated at a Level 1 trauma center. Each case of nerve injury was verified by chart review and excluded isolated digital nerve and other cutaneous nerve injuries. Medical charts were reviewed to retrieve patient and injury data. Results: In total, 1957 patients were identified with firearm injuries; the nerve injury study sample included 86 patients (95% males) and 98 nerves injured. The most common upper extremity nerve injured was the radial and/or posterior interosseous nerve ( n = 30, 25%) and in the lower extremity, the sciatic nerve ( n = 15, 13%). Nerve transection was confirmed in 21% of cases by surgical exploration ( n = 19) or ultrasound imaging ( n = 2). Axonotmetic injuries were confirmed in 20% of cases and in total only 41% of patients had full spontaneous functional recovery. Compared to neurapraxia, neurotmesis injuries had a significantly increased likelihood of concomitant vascular injury ( P = .007) but not skeletal injuries ( P = .65). Injury severity score was not associated with nerve injury severity ( P = .27). Conclusion: Nerve transections due to firearm-related trauma occur more frequently than previously believed. Early identification and surgical management of nerve transection injuries is imperative.
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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 | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".