Facial Fractures of the Upper Craniofacial Skeleton Predict Mortality and Occult Intracranial Injury After Blunt Trauma
Bibliographic record
Abstract
PURPOSE: The aim of this article was to assess how regional facial fracture patterns predict mortality and occult intracranial injury after blunt trauma. METHODS: Retrospective chart review was performed for blunt-mechanism craniofacial fracture patients who presented to an urban trauma center from 1998 to 2010. Fractures were confirmed by author review of computed tomographic imaging and then grouped into 1 of 5 patterns of regional involvement representing all possible permutations of facial-third injury. Mortality and the presence of occult intracranial injury, defined as those occurring in patients at low risk at presentation for head injury by Canadian CT Head Rule criteria, were evaluated. Relative risk estimates were obtained using multivariable regression. RESULTS: Of 4540 patients identified, 338 (7.4%) died, and 171 (8.1%) had intracranial injury despite normal Glasgow Coma Scale at presentation. Cumulative mortality reached 18.8% for isolated upper face fractures, compared with 6.9% and 4.0% for middle and lower face fractures (P < 0.001), respectively. Upper face fractures were independently associated with 4.06-, 3.46-, and 3.59-fold increased risk of death for the following fracture patterns: isolated upper, combined upper, panfacial, respectively (P < 0.001). Patients who were at low risk for head injury remained 4 to 6 times more likely to suffer an occult intracranial injury if they had involvement of the upper face. CONCLUSIONS: The association between facial fractures, intracranial injury, and death varies by regional involvement, with increasing insult in those with upper face fractures. Cognizance of the increased risk for intracranial injury in patients with upper face fractures may supplement existing triage tools and should increase suspicion for underlying or impending neuropathology, regardless of clinical picture at presentation.
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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".