The epidemiology of mandibular fractures treated at the Toronto general hospital: A review of 246 cases.
Bibliographic record
Abstract
BACKGROUND: Mandibular fractures constitute a substantial proportion of cases of maxillofacial trauma. This study investigated the incidence, causes and treatment of mandibular fractures at a hospital in Toronto. METHODS: The medical records and radiographs for 246 patients treated for mandibular fracture at the Toronto General Hospital over a 51 2-year period (from 1995 to 2000) were reviewed. Data on the patients age, sex, smoking status, alcohol and drug use, mechanism of injury, treatment modality, and post-operative complications were recorded and assessed. RESULTS: Men 21 to 30 years of age sustained the most mandibular fractures. The ratio of males to females was 5:1. Most fractures were caused by violent assault (53.5%), followed by falls (21.5%) and sports activities (12.2%). Alcohol was a contributing factor at the time of injury in 20.6% of fractures for which this information was available. Nearly half of all cases were treated by open reduction (49.1%). Complications occurred in 5.3% of patients. CONCLUSION: The incidence and causes of mandibular fracture reflect trauma patterns within the community and, as such, can provide a guide to the design of programs geared toward prevention and treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".