P.109 Management of a maxillofacial, transclival penetrating injury
Bibliographic record
Abstract
Background: Penetrating traumatic injuries to the clivus are rare. We describe the case of a 79-year-old man who presented to the emergency room with a butter knife protruding from his left cheek. Imaging showed the blade entering just beneath the left zygoma and transecting the clivus to terminate within the prepontine cistern. The tip of the knife abutted the right anterior inferior cerebellar artery and lower basilar artery. Methods: He was brought to the interventional neuroradiology OR with knife in place, by a combined surgical team of ENT, neurosurgery, and neuroradiology. Under local anaesthetic and intravenous sedation, vascular access to the distal left vertebral artery was obtained and a balloon positioned. Traction was applied to the knife and the knife was successfully removed avoiding any angular or rotational movements. An immediate angiogram showed no evidence of arterial injury. Results: The patient recovered uneventfully and was discharged home with no neurological deficit. Follow-up CT/CTA was performed a month later and confirmed no pseudoaneurysm or other complication. Conclusions: Management of penetrating skull base injuries by a multidisciplinary surgical team is advisable. Vascular imaging is crucial. Positioning of balloons within large vessels close to the penetrating object is recommended to control bleeding that may occur on removal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".