Operative Techniques in Mandibular Fracture Repair
Bibliographic record
Abstract
BACKGROUND: Mandibular fractures are the most common facial fractures treated in the emergency setting, with significant variability in operative management across surgical specialties. Plastic and reconstructive surgery (PRS), otolaryngology (ENT), and oral and maxillofacial surgery (OMFS) each approach mandibular fracture repair with different philosophies, particularly regarding tooth extraction within the fracture line. However, few studies directly compare these practices. OBJECTIVE: This study assessed differences in operative techniques, specifically tooth extraction and fixation strategies, across PRS, ENT, and OMFS in the treatment of isolated mandibular fractures at a level 1 trauma center. METHODS: Following institutional review board approval, a retrospective chart review was conducted at Regional One Health from May 2019 to May 2020. Ninety patients with isolated mandibular fractures were identified using relevant Current Procedural Terminology codes. Statistical analysis included χ2 and analysis of variance testing with significance set at P < 0.05. RESULTS: Among 90 patients (80% male; mean age, 33 years), assault was the leading cause of injury. These cases were managed by 3 specialties: ENT (24 patients), PRS (24 patients), and OMFS (42 patients). All 3 specialties utilized MMF and ORIF with similar frequency. However, OMFS demonstrated significantly higher tooth extraction rates (50%) compared with ENT (8%) and PRS (4%) (P < 0.00005). ENT had the longest average time to surgery (9 days) compared with PRS (2 days) and OMFS (1 day). No significant differences were observed in reoperation rates, operative duration, or hospital stay among the specialties. CONCLUSIONS: Significant differences were identified in the frequency of surgical tooth extractions and time to operation for mandibular fracture repairs across different specialties. These differences may impact resource allocation and patient outcomes. Further research is needed to explore the origins of these variations and their long-term effects.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".