Design and evaluation of a new synthetic brain simulator for endoscopic third ventriculostomy
Bibliographic record
Abstract
OBJECT: Endoscopic third ventriculostomy (ETV) is an effective but technically demanding procedure with significant risk. Current simulators, including human cadavers, animal models, and virtual reality systems, are expensive, relatively inaccessible, and can lack realistic sensory feedback. The purpose of this study was to construct a realistic, low-cost, reusable brain simulator for ETV and evaluate its fidelity. METHODS: A brain silicone replica mimicking normal mechanical properties of a 4-month-old child with hydrocephalus was constructed, encased in the replicated skull, and immersed in water. Realistic intraventricular landmarks included the choroid plexus, veins, mammillary bodies, infundibular recess, and basilar artery. The thinned-out third ventricle floor, which dissects appropriately, is quickly replaceable. Standard neuroendoscopic equipment including irrigation is used. Bleeding scenarios are also incorporated. A total of 16 neurosurgical trainees (Postgraduate Years 1-6) and 9 pediatric and adult neurosurgeons tested the simulator. All participants filled out questionnaires (5-point Likert-type items) to rate the simulator for face and content validity. RESULTS: The simulator is portable, robust, and sets up in minutes. More than 95% of participants agreed or strongly agreed that the simulator's anatomical features, tissue properties, and bleeding scenarios were a realistic representation of that seen during an ETV. Participants stated that the simulator helped develop the required hand-eye coordination and camera skills, and the training exercise was valuable. CONCLUSIONS: A low-cost, reusable, silicone-based ETV simulator realistically represents the surgical procedure to trainees and neurosurgeons. It can help them develop the technical and cognitive skills for ETV including dealing with complications.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".