Technological refinements in transoral laser exoscopic surgery: the VITOM EAGLE
Bibliographic record
Abstract
Traditionally, microscopes have been the primary magnification tools in head and neck surgery, offering excellent illumination, augmentation, and three-dimensional (3D) visualisation. More recently, exoscopes such as the VITOM 3D from Karl Storz, have emerged. These devices utilise high-definition cameras to project 3D views onto a screen, enabling the entire surgical team to observe the procedure while the first operator assumes a more ergonomic position during surgery. VITOM 3D enhances surgical accuracy and team collaboration, although it presents challenges including difficulties with focus at high magnification and less stable coupling with adjunctive tools. To address these limitations, the VITOM EAGLE exoscope was recently introduced. Weighing 4 kg, it features a 6X optical and 2X digital zoom, 4K resolution, stepless focus adjustment, and a 90° viewing angle. It offers improved ergonomics even compared to the previous VITOM 3D, better laser coupling stability, and enhanced visualisation capabilities, especially at higher magnification. The VITOM EAGLE’s controls include an IMAGE1 PILOT, footswitch, and head buttons, managing functions like focus, zoom, brightness, and image capture. Despite its advancements, some details, like the smoothness of the robotic arm, still require refinement. In our clinical practice, several cases were managed with the use of the VITOM 3D and, more recently, with VITOM EAGLE, some of which are illustrated herein.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".