Neuronavigation in Endonasal Pituitary and Skull Base Surgery Using an Autoregistration Mask without Head Fixation: An Assessment of Accuracy and Practicality
Bibliographic record
Abstract
BACKGROUND: Intraoperative navigation is an important tool used during endonasal surgery, which typically requires rigid head fixation. Herein we describe a navigational technique using an autoregistration mask without head fixation. MATERIAL AND METHODS: Prospective evaluation of a surface autoregistration mask used without rigid head fixation in 12 consecutive endonasal endoscopic skull base procedures was performed with patients positioned in a horseshoe head holder. We assessed the accuracy by recording the surface registration error (SRE) and target registration error (TRE). We also noted the time required for installation and the occurrence of system failure. The system's accuracy was validated using a deep target simultaneously viewed with endoscopic. RESULTS: In 12 consecutive endonasal cases performed by a neurosurgeon and ENT team, pathologies included pituitary macroadenomas (9), chordoma (1), craniopharyngioma (1), and sinonasal melanoma (1). Median time required for the registration and accuracy verification was 84 seconds (interval 64 to 129 seconds). The mask stayed on the patient throughout the procedure. The mean SRE was 0.8 mm (interval 0.6 to 0.9 mm). The mean TRE was 0.9 ± 0.7 mm and 1.0 ± 0.8 mm measured respectively at the beginning and end of the case. In every case, the system was judged accurate by the surgical team using the sphenoid keel or an intrasphenoidal bony septation as a deep target for internal validation. No system failure occurred during these 12 cases. CONCLUSION: A facial surface autoregistration mask maintained in place throughout surgery without rigid head fixation allows excellent operational accuracy in endonasal pituitary and skull base surgery. This navigation system is practical, reliable, and noninvasive.
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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 | 0.002 | 0.007 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".