Advanced Cranial Navigation
Bibliographic record
Abstract
BACKGROUND: Cranial surgical navigation is most commonly performed by registration with fiducial markers, optic tracking, and intermittent pointer-based application. OBJECTIVE: To assess the accuracy and applicability of an advanced cranial navigation setup. METHODS: Continuous electromagnetic instrument navigation was used in 136 neurosurgical cases with a standard navigation system. A phantom head in an intraoperative magnetic resonance imaging environment was used to compare the accuracy of the advanced and standard navigation setups. RESULTS: A navigated suction device was used in 71 cases of intracranial tumor surgery and 46 cases of endoscopic transsphenoidal surgery. The ventriculoscope was navigated in 6 cases and the stereotactic biopsy needle in 4 cases. Electromagnetic tracking was used for catheter placement in 9 cases. The learning curve comprised 6 of the 136 cases during the first month of application. No significant difference was observed at the intracranial target points between the standard navigation setup using optic tracking, fiducial marker registration, and pointer and the advanced navigation setup with electromagnetic tracking, surface-based registration, and navigation of a field-detecting stylet in a standard metal suction tube when performed outside the 5-G line of the 3.0-T intraoperative magnetic resonance imaging. CONCLUSION: Continuous instrument navigation is the prerequisite for seamless integration of navigation systems into the neurosurgical operating workflow. Our data confirm that the application of preoperative imaging, surface-merge registration, and continuous electromagnetic tip-tracked instrument navigation may provide such integration without a significant reduction in accuracy compared with standard navigation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".