Robotically-Operated Video Optical Telescopic-microscopy Resection of an Arteriovenous Malformation With Port-Assisted Intraoperative Surgical Devascularization: 2-Dimensional Operative Video
Bibliographic record
Abstract
Surgical management of cerebral arteriovenous malformations (AVMs) remains challenging carrying significant postoperative neurological morbidities. Advancements in visualization together with minimally invasive techniques have the promise of reducing this morbidity. In this video, grounded on a series of purpose-built cadaveric dissections, the integration of preoperative 3-dimensional diffusion tensor imaging rendering/planning, and a port-based transsulcal/para-fascicular approach coupled with a robotically operated video optical telescopic-microscopy system (ROVOT-m; Synaptive Medical, Toronto, Canada) and intraoperative neuronavigation for the resection of a deep-seated AVM, was demonstrated. The authors present the case of a 30-yr-old right-handed male with nausea, vomiting, dizziness, visual field deficits, and conductive aphasia. Informed consent was obtained from the patient for publication of this surgical video. CTA/Angiograms revealed a 6-cm Spetzler-Martin Grade-V left occipital-temporal-parietal AVM/dural-arteriovenous fistula. Surgical planning via CT/MRI/CTA/Color-Doppler was undertaken. The patient underwent angiographic embolization but a significant portion of the deep feeder arteries, particularly the choroidal arteries and the distal middle and posterior cerebral arteries remained, and could not be effectively embolized. The pedicle in the atrial trigone was targeted and accessed via the parieto-occipital sulcus via the port, in order to provide early control of the deep portion. The port was advanced into the sulcus extrapially then travelled through the U-fibers, along the long axis/parallel (para-fascicular) to white-matter fascicles. Following deep-pedicle devascularization, the port was removed, dura was widely opened, and remaining resection was undertaken using the ROVOT-m exclusively. No perioperative complications occurred and recovery of aphasia and improvement in visual fields was evident in a short-term follow-up. Still following principles of microsurgery, this approach and collaborative technologies, has the potential to enhance safety and efficiency in the surgical management of complicated cerebrovascular lesions.Watch now at https://academic.oup.com/ons/article-lookup/doi/10.1093/ons/opx267 {"href":"Single Video Player","role":"media-player-id","content-type":"play-in-place","position":"float","orientation":"portrait","label":"Robotically Operated Video Optical Telescopic-microscopy Resection of an Arteriovenous Malformation With Port-Assisted Intraoperative Surgical Devascularization: 2-Dimensional Operative Video","caption":"","object-id":[{"pub-id-type":"doi","id":""},{"pub-id-type":"other","content-type":"media-stream-id","id":"1_0v01omun"},{"pub-id-type":"other","content-type":"media-source","id":"Kaltura"}]} Disclosures Dr Kassam has the following disclosures: (1) Synaptive Medical (consultant), (2) KLS Martin (consultant), and (3) Medtronic Medical (advisory board). This research is supported by an award to the Aurora Research Institute by the Vince Lombardi Cancer Foundation. We would like to thank Nico Corporation, Carl Zeiss, Synaptive Medical, Stryker Medical, and Karl Storz for their donations that made our research possible in the Neuroanatomy Laboratory.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".