Epidemiology of Maxillofacial Injuries at Trauma Hospitals in Ontario, Canada, Between 1992 and 1997
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to review the epidemiology of maxillofacial skeletal injuries in severely injured patients admitted to trauma hospitals in Ontario, Canada, with an Injury Severity Score > 12. METHODS: The Ontario Trauma Registry was accessed to examine the epidemiology of maxillofacial skeletal injuries in severely injured patients treated at 12 trauma hospitals in the province of Ontario, Canada, between 1992 and 1997. Data were collected prospectively, and a descriptive analysis was performed to determine the pattern of maxillofacial injuries, including patient age, sex distribution, etiology of injury, time of injury, and injury profile. RESULTS: There were 2,969 patients that met the inclusion criteria. The median age was 25 years, and men were injured at a 3:1 ratio over women. Most severely injured patients with maxillofacial fractures were injured as a result of motor vehicle collision (70%), with only 33% of the patients restrained with a seat-belt. The temporal distribution of injuries showed that most injuries occurred during evening hours, on weekends, and in the summer. The largest number of fractures was found in the maxilla and orbital bones. The Injury Severity Score of the patients in this study ranged from 13 to 75, with a median of 25. The injury most commonly associated with maxillofacial fractures was injury to the head and neck area. Of patients with injury to the head and neck, most had an altered level of consciousness or injuries to the skull, brain, or cranial vessels. CONCLUSION: Many severely injured patients have maxillofacial injuries. Long-term collection of epidemiologic data regarding maxillofacial fractures is important for the evaluation of existing preventative measures and useful in the development of new methods of injury prevention. Furthermore, insight into the epidemiology of facial fractures and concomitant injuries is an integral component in evaluating the quality of patient care, developing optimal treatment regimens, and making decisions regarding appropriate resource and manpower allocations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".