P.050 Minimally invasive disconnection of spinal dural arteriovenous fistulas in a hybrid neurovascular operating room
Bibliographic record
Abstract
Background: Hybrid neurovascular operating rooms offer significant advantages for vascular neurosurgery. In 2008, we installed North America’s first robotic intraoperative rotational 2D/3D angiography unit in a neurosurgery operating room. To date, 200 procedures have been performed. Methods: In selected cases of spinal dural arteriovenous fistula (dAVF)requiring surgical disconnection, intraoperative spinal angiographic roadmapping, angiographic image overlay onto the skin and surgically exposed spine, and laser cross-hair image guidance were utilized to accurately determine the location and trajectory of the draining vein. Results: In four cases of spinal dAVF, a minimally invasive approach was employed, via either single-level (N=2) or two-level (N=1) hemilaminectomy. Techniques used included: angiographic roadmap / image overlay and intraoperative fluoroscopic with laser light guidance. These provided sub-centimeter accuracy in localizing the path of the draining vein. Surgical incision lengths ranged between 4 to 5 cm, with the shortest incision measuring only 4.2 cm. Complete cure was obtained in all cases, with no untoward complications. Conclusions: Hybrid neurovascular operating room technology can facilitate the use of minimally invasive approaches to spinal dural AVF disconnection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".