Maxillofacial Fractures: A Three-Year Survey
Bibliographic record
Abstract
Background: Maxillofacial fractures constitute a substantial proportion of trauma globally. The main causes worldwide are road traffic accidents (RTAs), falls, assaults, sports, firearm injuries and industrial trauma. The highest incidence is commonly seen in the young age group with majority being male. The most common site in maxillofacial injuries is the mandible followed by the zygomatic complex, maxilla, and alveolar process. Maxillofacial trauma also poses a significant socioeconomic burden on affected individuals. Hence appropriate treatment and prevention of these morbidities and possible mortality is necessary. This study is therefore aimed at analyzing the prevalence, pattern of presentation of maxillofacial injuries at Lagos State University Teaching Hospital (LASUTH) in Western Nigeria. Methods: A retrospective review of 182 patients diagnosed and treated for maxillofacial injuries at the Oral and Maxillofacial Department of the LASUTH was conducted. Data were obtained from clinical notes and records of radiological findings noting patient ’s age, gender, etiologic factors (RTA, assault, sport, and fall), anatomic site of injury and different definitive treatment modalities. The data were analyzed by SPSS version 20 using various descriptive statistical tools. Mean and standard deviation were calculated for quantitative variable like age while frequency and percentage were calculated for qualitative variables like gender and site of fracture. Results: Majority of patients were male (72.0%) with a male to female ratio of 1:0.4. Most patients were between 31 and 40 (34.1%) years of age. RTA accounting for 73.1% of the injuries was the most common cause for maxillofacial injury followed by assault (19.2%). Majority of injuries due to RTA were of motorcycles accidents (33.6%). The most common sites of fracture out of 226 sites were in the mandible (62.8%, P = 0.003). Among the mandibular fracture sites, 28.2% affected the body of the mandible. Majority (31.9%) of the studied patients presented within 24 h (<= 1 day). Out of the 182 patients, 68.1% were treated by close reduction. Conclusions: RTA represented the major etiological factor of maxillofacial injuries. The mandible remains the most affected bone of the facial skeleton. Closed reduction is the most common approach used for treatment. J Curr Surg. 2019;9(4):51-56 doi: https://doi.org/10.14740/jcs387
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".