Seis anos de atendimento em trauma facial: análise epidemiológica de 355 casos
Bibliographic record
Abstract
UNLABELLED: Facial traumas are frequent in emergencies, and they require the diagnosis of fractures and associated lesions. AIM: To analyze epidemiological data concerning facial trauma care. MATERIALS AND METHODS: Three hundred and fifty-five charts from patients with facial trauma treated by the Service of Otorhinolaryngology, from January 2002 to December 2008, were revised. The following data was collected: age, gender, etiology, anatomical localization of the fracture, associated injuries, alcohol consumption, treatment, and hospitalization. STUDY DESIGN: A retrospective historical longitudinal study. RESULTS: Most of the patients are young adult men (p<0.05) with a male:female ratio of 4:1(p<0.05). Interpersonal violence is the most prevalent cause of facial trauma (27.9%), followed by motor vehicle accidents (16.6%) (p<0.05). The mandible is the most prevalent facial bone fractured (44.2%), followed by nasal fracture (18.9%) (p<0.05). 41.1% of the patients consumed alcohol with a male:female ratio of 11.2:1 (p<0.05). Seventy-seven percent of the patients required surgical intervention (p<0.05) and 84.5% were hospitalized (p<0.05). CONCLUSION: Young male adults are the most prevalent victims of facial trauma, and interpersonal violence is responsible for the majority of the facial injuries. Most of the cases of facial trauma are associated with the consumption of alcohol. Further studies will be necessary to provide a clear understanding of the trends in the etiology of facial 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".