Prevalence of Different Kinds of Maxillofacial Fractures and Their Associated Factors Are Surveyed in Patients
Bibliographic record
Abstract
INTRODUCTION: Nowadays maxillofacial fractures have increased. In this study prevalence of different kinds of maxillofacial fractures and their associated factors are surveyed in patients referred to Imam Khomeini Hospital, Urmia in 2011. METHODS: The study was across-sectional observational study. 637 cases of patients with a confirmed diagnosis of maxillofacial fractures in 2011 referred to Imam Khomeini Hospital, Urmia and their data records were analyzed using SPSS software and chi-square tests. RESULTS: In this study, 457 patients were male and 178 were female and the mean age was 14.47 ± 26.68 years. Falling was the most common cause of fractures after accidents and assaults were the most common causes. The most common site of nasal fractures was about 66.4% and then fractures in several places about 14.9% and mandibular 7.1%. CONCLUSION: Based on the results obtained in the present study with other studies in this area it is concluded that maxillofacial fractures in males and in 20 to 30 years of age is prevalent and is mostly due to falling and road accidents and are further seen in nasal bone and mandible.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".