Identification of Mandibular Fracture Epidemiology in Canada: Enhancing Injury Prevention and Patient Evaluation
Bibliographic record
Abstract
BACKGROUND: Mandibular fractures can lead to significant functional and aesthetic sequelae if treated improperly. They may act as an indicator of concomitant trauma and are very demanding on the public health care system. Thus, knowledge of mandibular fracture epidemiology is critical to effective prevention, as well the establishment of accurate trauma evaluation protocols. OBJECTIVES: To identify the epidemiology of mandibular fractures treated at a level 1 Canadian trauma centre, clarify the pathogenesis of these epidemiological patterns and suggest potential targets for preventive efforts. METHODS: A retrospective review of all mandibular fracture patients presenting to the Montreal General Hospital between 1998 and 2003 was performed. Medical records and digitized radiographic imaging were used to collect patient demographics and injury data. RESULTS: The chart review identified 181 patients with 307 mandibular fractures. Fifty-two per cent of the fractures occurred in individuals 21 to 40 years of age, 78% of patients were male, and there was wide ethnic diversity. Sixty percent of patients had multiple mandibular fractures; 29% were symphyseal/parasymphyseal fractures, 25% were condylar fractures and 23% were angle fractures. Assault was the most common mechanism of injury, with 29% of fractures involving alcohol or illegal drug use. Thirty percent of patients had an associated facial fracture, and more than one-third had another major injury. CONCLUSIONS: The present epidemiological review reveals several potential prevention targets as well as significant trends. Further research into the impact of these preventive measures could more objectively identify their impact on 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 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".