Incidence of Major and Minor Brain Injuries in Facial Fractures
Bibliographic record
Abstract
BACKGROUND: Facial fractures can be associated with brain and cervical spine injuries because impact forces are transmitted through the head and neck. Although major brain injury is commonly recognized in these patients, incidence of minor brain injury is not well-known, despite potential morbidity and mortality. OBJECTIVES: This prospective study aimed to determine the incidence of both major and minor brain injuries in 100 patients presenting to a craniofacial surgery service with facial fractures and to identify characteristics associated with brain injury. METHODS: Data were collected for a 9-month period by a craniofacial surgeon at a level I trauma center. A questionnaire and checklist were designed to capture information about major and minor brain injury in patients with facial fractures. Assessments were completed in the outpatient clinic, emergency department, hospital ward, or intensive care unit during the first patient encounters. RESULTS: The average age of patients was 34 years; 79% were male. Time between injury and assessment ranged from less than a few hours to 4 months. Incidence of brain injury was 67% overall: 29% with major brain injury and 38% with minor injury. Major brain injury was commonly diagnosed early in the emergency department or intensive care unit. Conversely, minor brain injury tended to be diagnosed late in the clinic. Patient age, mechanism of injury, and type of facial fracture predicted brain injuries overall, but mechanism of injury was the sole predictor of minor brain injury. CONCLUSIONS: Facial fractures are often associated with brain injury. A high level of suspicion is warranted for minor traumatic brain injuries.
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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 | 0.001 | 0.000 |
| 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".