Traumatic Brain Injury in Trauma Patients With Isolated Facial Fractures
Bibliographic record
Abstract
BACKGROUND: Diagnosis of traumatic brain injury (TBI), and specifically mild TBI (mTBI), is a diagnostic challenge which can delay diagnosis preventing early intervention and follow-up care. Facial fractures represent an objective surrogate marker for potential force transmission to the neural cavity. The authors' objective was to characterize the prevalence of TBI in trauma patients with isolated facial fractures stratified by injury severity. METHODS: The authors performed a retrospective cross-sectional study of the National Trauma Databank (NTDB) from 2007 to 2014 assessing a total of 1,867,761 participants identified as having a TBI and 306,785(60.2%) had an isolated facial fracture using ICD-9 codes. TBI severity was subdivided using Glasgow Coma Scale into mTBI and moderate-to-severe TBI. Logistic regression assessed odds of mTBI and moderate-to-severe TBI with different isolated facial fractures adjusted for injury severity. RESULTS: Trauma patients with isolated facial fractures of the nasal bone, mandible, malar region and maxilla, orbital floor, and alveolar and palate had a concomitant prevalence of mTBI ranging from 21.3% to 46.0% and moderate-to-severe TBI ranging from 7.3% to 18.4%. Mandibular fractures had the lowest odds of mTBI and moderate to severe TBI while alveolar and palate fractures had the highest odds of mTBI [OR3.20,95%CI (3.11-3.30)] and moderate to severe TBI [OR3.83,95%CI (3.65-4.01)]. CONCLUSIONS: Isolated facial fractures have a high prevalence of mTBI at all injury severity levels. Clinicians can use the presence of facial fractures in trauma patients to serve as clinical markers for TBI, without distracting from already existing trauma protocols and their focus on treatment of immediate life-threatening injuries raising both awareness and potential for early intervention.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".