Three-Dimensional Simulation of Collision-Free Paths for Combined Endoscopic Third Ventriculostomy and Pineal Region Tumor Biopsy
Bibliographic record
Abstract
BACKGROUND: Recent innovations to expand the scope of intraventricular neuroendoscopy have focused on transitioning multiple-incision procedures into single-corridor approaches. However, the successful adoption of these combined procedures requires minimizing the unwanted torques applied to surrounding healthy structures. OBJECTIVE: To define the geometry of relevant anatomical structures in endoscopic third ventriculostomy (ETV) and pineal region tumor biopsy (ETB). Second, to determine the optimal instrument shaft path required for collision-free single burr hole combined ETV/ETB. METHODS: Magnetic resonance and computed tomography data from 15 pediatric patients who underwent both ETV and ETB procedures between 2006 and 2014 was segmented by using the 3DSlicer software package to create virtual 3-D patient models. Anatomical regions of interest were measured including the foramen of Monro, the massa intermedia, the floor of the third ventricle, and the tumor margin. Utilizing the MATLAB software package, virtual dexterous instruments were inserted into the models and optimal dimensions were calculated. RESULTS: The diameters of the foramen of Monro, massa intermedia (anterior-posterior, superior-inferior), anterior third ventricle, and tumor margin are 6.85, 4.01, 5.05, 14.2, and 28.5 mm, respectively. The average optimal burr placement was determined to be 22.5 mm anterior to the coronal and 30 mm lateral to the sagittal sutures. Optimal flexible instrument geometries for novel instruments were calculated. CONCLUSION: We have established a platform for estimating the shape of novel curved dexterous instruments for collision-free targeting of multiple intraventricular points, which is both patient and tool specific and can be integrated with image guidance. These data will aid in developing novel dexterous instruments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".