Transsphenoidal surgery: accuracy of an image‐guided neuronavigation system to approach the pituitary fossa (sella turcica)
Bibliographic record
Abstract
OBJECTIVE: To determine the accuracy of locating the pituitary fossa with the Brainsight neuronavigation system by determining the mean target error of the rostral (tuberculum sellae) and caudal (dorsum sellae) margins of the pituitary fossa. STUDY DESIGN: Experimental cadaveric study. ANIMALS: Ten canine cadavers. METHODS: Computed tomography (CT) and MRI were performed on each cadaver with fiducials in place. Images were saved to the neuronavigation computer and used to plan the drilling approach. The cadavers were placed in the surgical head clamp of the Brainsight system and positioned for a transsphenoidal approach. On the basis of the planning, 2 localization points were drilled, 1 each at the rostral and caudal margins of the pituitary fossa, and CT was repeated. Error was assessed from the difference in millimeters between the targets identified during Brainsight planning and the actual location of the 2 points drilled on each cadaver skull as identified by postdrilling CT. RESULTS: The rostral and caudal margins of the pituitary fossa provided 2 target points per cadaver. The median target error (interquartile range) for all target sites (n = 20) was 3.533 mm (range, 2.013-4.745). CONCLUSION: This stereotactic system allowed the surgeon to locate the rostral and caudal margins of the pituitary fossa with clinically acceptable accuracy and confidence. CLINICAL SIGNIFICANCE: Using the Brainsight neuronavigation system for localization during transsphenoidal hypophysectomy may decrease morbidity and surgical time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".