Does stereo-endoscopy improve neurosurgical targeting in 3<sup>rd</sup>ventriculostomy?
Bibliographic record
Abstract
Endoscopic third ventriculostomy is a minimally invasive surgical technique to treat hydrocephalus; a condition where patients suffer from excessive amounts of cerebrospinal fluid (CSF) in the ventricular system of their brain. This technique involves using a monocular endoscope to locate the third ventricle, where a hole can be made to drain excessive fluid. Since a monocular endoscope provides only a 2D view, it is difficult to make this perforation due to the lack of monocular cues and depth perception. In a previous study, we had investigated the use of a stereo-endoscope to allow neurosurgeons to locate and avoid hazardous areas on the surface of the third ventricle. In this paper, we extend our previous study by developing a new methodology to evaluate the targeting performance in piercing the hole in the membrane. We consider the accuracy of this surgical task and derive an index of performance for a task which does not have a well-defined position or width of target. Our performance metric is sensitive and can distinguish between experts and novices. We make use of this metric to demonstrate an objective learning curve on this task for each subject.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".