An Algorithm for Managing Intraosseous Vascular Anomalies of the Craniofacial Skeleton
Bibliographic record
Abstract
BACKGROUND: Intraosseous vascular anomalies (IOVA) are rare in the craniofacial skeleton and present a diagnostic and therapeutic challenge. This study aims to describe the clinical management based on a large case series. METHODS: A retrospective chart review was performed and 9 IOVA were identified over a 15-year period. Data on demographics, diagnostic features, clinical management, and outcomes were reviewed. RESULTS: Five frontal bone IOVA and 4 orbital IOVA were identified. The postoperative follow-up ranged from 4 months to 4 years. All 9 lesions were diagnosed with computed tomography (CT) imaging. Magnetic resonance imaging (MRI) was used to delineate soft tissue involvement in 2 patients presenting with oculo-orbital dystopia and ophthalmoplegia. En bloc excision was performed in all patients. Preoperative interventional embolization was critical in the successful resection of an orbital IOVA following 2 previously failed attempts that were aborted secondary to hemorrhage. Intraoperative 3-dimensional stereotactic navigation was used for the accurate en bloc excision of a frontal IOVA to prevent injury to the frontal sinus. Reconstruction of esthetic and functional deformities was successfully accomplished. CONCLUSION: The diagnosis of IOVA relies primarily on clinical assessment and CT imaging. Further interpretation of the involvement of periorbital, facial, and intracranial soft tissue is best defined by MRI. Multidisciplinary care with interventional radiology and neurosurgery must be considered for ensuring the safe and adequate en bloc excision of craniofacial IOVA.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".