The le fort system revisited: trauma velocity predicts the path of le fort i fractures through the lateral buttress
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of trauma velocity on the pattern of Le Fort I facial fractures. METHOD: A retrospective medical record review was conducted on a consecutive cohort of craniofacial traumas surgically treated by a single surgeon between 2007 and 2011 (n=150). Of these cases, 39 Le Fort fractures were identified. Patient demographic information, method of trauma and velocity of impact were reviewed for these cases. Velocity of impact was expressed categorically as either 'high' or 'low': high-velocity fractures were those caused by a fall from >1 story or a motor vehicle collision; low-velocity fractures were the result of assaults with a blunt weapon, closed fist or falls from standing height. The vertical position of each fracture was measured at its point of entry on the lateral buttress and its point of exit on the piriform aperture. To allow for comparison across individuals, values were expressed as ratios based on their location on the face relative to these landmarks. A Wilcoxon rank-sum test was used to compare the fracture heights caused by high- and low-velocity trauma. RESULTS: The results revealed that high-velocity traumas to the face create Le Fort I fractures at a higher point in the lateral buttress compared with low-velocity traumas. There was no difference between heights at the piriform aperture. CONCLUSION: High-velocity trauma resulted in higher Le Fort I fracture patterns compared with low-velocity 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 | 0.000 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".