Localized Intraoperative Virtual Endoscopy (LIVE) for Surgical Guidance in 16 Skull Base Patients
Bibliographic record
Abstract
IMPORTANCE: Previous preclinical studies of localized intraoperative virtual endoscopy-image-guided surgery (LIVE-IGS) for skull base surgery suggest a potential clinical benefit. OBJECTIVE: The first aim was to evaluate the registration accuracy of virtual endoscopy based on high-resolution magnetic resonance imaging under clinical conditions. The second aim was to implement and assess real-time proximity alerts for critical structures during skull base drilling. DESIGN AND SETTING: Patients consecutively referred for sinus and skull base surgery were enrolled in this prospective case series. PARTICIPANTS: Five patients were used to check registration accuracy and feasibility with the subsequent 11 patients being treated under LIVE-IGS conditions with presentation to the operating surgeon (phase 2). INTERVENTION: Sixteen skull base patients were endoscopically operated on by using image-based navigation while LIVE-IGS was tested in a clinical setting. MAIN OUTCOME AND MEASURES: Workload was quantitatively assessed using the validated National Aeronautics and Space Administration Task Load Index (NASA-TLX) questionnaire. RESULTS: Real-time localization of the surgical drill was accurate to ~1 to 2 mm in all cases. The use of 3-mm proximity alert zones around the carotid arteries and optic nerve found regular clinical use, as the median minimum distance between the tracked drill and these structures was 1 mm (0.2-3.1 mm) and 0.6 mm (0.2-2.5 mm), respectively. No statistical differences were found in the NASA-TLX indicators for this experienced surgical cohort. CONCLUSIONS AND RELEVANCE: Real-time proximity alerts with virtual endoscopic guidance was sufficiently accurate under clinical conditions. Further clinical evaluation is required to evaluate the potential surgical benefits, particularly for less experienced surgeons or for teaching purposes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".