Rapid Intraoperative Zygoma Fracture Imaging
Bibliographic record
Abstract
BACKGROUND: A fractured zygoma frequently results in an aesthetically displeasing facial asymmetry. Open reduction and internal fixation may accurately realign the facial skeleton but often with undesirable sequelae. The authors' objective was to develop a precise technique of intraoperative zygoma fracture imaging using a C-arm to permit anatomical fracture realignment while reducing the extent of skeletal exposure required. The simplicity and accessibility of this method should allow its widespread clinical application. METHODS: First, using a model skull, the relative positions of the C-arm required to adequately depict zygoma projection, width, arch contour, and zygoma rotation were defined. Second, diverse zygoma fracture types were created in six cadaver heads with a Mini Bionix machine and were repaired using C-arm guidance; accuracy was confirmed with postoperative computed tomography. Third, after defining optimal operating room setup, the accuracy in a clinical case was assessed. RESULTS: Two C-arm views were defined. The zygoma projection view (C-arm at 70 to 90 degrees to the skull's coronal plane) allows visualization of projection, width, and contour. The rotation view (C-arm at 70 to 90 degrees to the skull's sagittal plane) allows visualization of zygoma rotation. Postoperative computed tomographic imaging confirmed anatomical repair in all cases. Average operating room duration was less than 30 minutes, with operating room times decreasing progressively. CONCLUSIONS: The authors have developed an accurate technique of intraoperative zygoma fracture imaging and reduction guidance. This technique may decrease the risks of open access by potentially limiting direct skeletal exposure to buttresses where skeletal stabilization is required. In addition, this method is simple, can be learned and used rapidly, and is readily accessible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".