P.083 Use of jugular venous pressure to optimize outcomes of vestibular schwannoma resection: a review of the literature and proof of concept
Bibliographic record
Abstract
Background: Surgical resection of vestibular schwannoma (VS) is often curative if gross total resection is achieved, however, it is a delicate procedure with high risk to the facial nerve. With retrosigmoid approach for resection, the head is positioned to maximize lateral head rotation and neck flexion in order to optimize the surgical field. However, this may inadvertently occlude cerebral venous drainage, elevating intracranial pressure (ICP) and increasing intraoperative bleeding. Methods: Here, we review relevant literature regarding the effects of head rotation and neck flexion on internal jugular vein (IJV) occlusion and ICP, and highlight the notion that head rotation and flexion may occlude the ipsilateral IJV, increasing ICP. Subsequently, we propose a novel technique using continuous, real-time monitoring of jugular bulb pressure (JBP) to detect obstructions in jugular venous flow and guide optimal head positioning prior to VS resection. Results: As proof of concept, we present a case in which JBP monitoring was employed to optimize head positioning prior to a VS resection, which shows a significant reduction in JBP compared to traditional positioning. Conclusions: This innovative approach offers promise in enhancing the safety and efficacy of intracranial surgery for VS and potentially other neurosurgical procedures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".