Quantitative Analysis of Surgical Working Space During Endoscopic Skull Base Surgery
Bibliographic record
Abstract
Abstract Objectives Selective dissection of intranasal anatomy may improve visualization and maneuverability at the skull base. We aimed to quantify the dimensions of working space and angles achieved following sequential removal of intranasal structures using an endoscopic transphenoidal approach to the skull base. Methods Cone beam computed tomography scans of four cadaveric heads were obtained for registration of an optical tracking system. Each head was sequentially dissected: (1) sphenoidotomy and limited posterior septectomy, (2) unilateral partial middle turbinectomy, (3) bilateral partial middle turbinectomy, and (4) wide posterior septectomy. The maximal craniocaudal and mediolateral distance (mm) and angle (degrees) reached were calculated at the sphenoid face and sella. Data were analyzed using descriptive statistics and tests of statistical significance. The significance level was set at p ≤ 0.05. Results A significant improvement in both dimensions of working space was observed with each stage of dissection at the level of the sphenoid face. Maximal working space was achieved following bilateral middle turbinectomy and wide posterior septectomy with a 38 and 29% increase in working space in the mediolateral and craniocaudal dimensions, respectively. The largest stepwise increase in working space was observed with unilateral middle turbinectomy (mediolateral: 24 ± 3 mm and craniocaudal: 20 ± 3 mm). A trend toward improved degrees of visualization was observed with each stage of dissection but was not statistically significant. Conclusion Approaches to the skull base can be enhanced by selective unilateral/bilateral partial middle turbinectomy and posterior septectomy being performed to improve visualization and maximize surgical working freedom.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".