P60. Presentation And Management of Ballistic Mandibular Fractures
Bibliographic record
Abstract
PURPOSE: Ballistic mandibular fractures can cause devastating injury. However, there is a lack of data regarding their management. Here, we examine the management and outcomes of patients with ballistic mandibular fractures. METHODS: A retrospective study was conducted reviewing patients with firearm-related mandibular injury presenting to a Level 1 Trauma Center from 2018-2023. RESULTS: 90 patients were included. The average age was 29.3 ± 13.2 years and 74 (82.2%) were male. 82 (91.1%) presented post-assault, 6 (6.7%) after self-inflicted injury, and 2 (2.2%) after accidental injury. Body fractures were the most common (40.0%) followed by ramus (25.6%) and angle (13.3%) fractures. 47 patients (52.2%) also had non-facial injuries. On presentation, 68 patients (75.6%) were intubated; 34 (37.8%) of them later required tracheostomy. 40 patients (44.4%) presented with significant bleeding; 14 (15.6%) were taken to the operating room and 8 (8.9%) were taken for embolization. 67 patients (74.4%) required surgical repair; 48 of them (71.6%) were repaired with internal fixation, 16 (23.9%) required bone grafts, 45 (67.2%) were placed in maxillomandibular fixation, and 3 (3.3%) required microvascular reconstruction. 14 patients (15.6%) had complications post-operatively; 2 (2.2%) had wound healing complications, 6 (6.7%) had infection, 3 (3.3%) had exposed hardware, 3 (3.3%) had malunion, and 1 (1.1%) had palatal fistula and carotid-cavernous fistula, respectively. 12 (13.3%) required surgery for complications. Neurologic complications led to mortality in 6 (6.7%) patients. CONCLUSION: Ballistic mandible fractures present a unique challenge. Our study is one of the few to examine management and complications of ballistic mandibular trauma.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".