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Record W2136091148 · doi:10.1177/0037549713491519

Endoscopic third ventriculostomy on a microneurosurgery simulator

2013· article· en· W2136091148 on OpenAlexafffund
Di Jiang, Jordan Hovdebo, Anne Cabral, Vincent Mora

Bibliographic record

VenueSIMULATION · 2013
Typearticle
Languageen
FieldNeuroscience
TopicCerebrospinal fluid and hydrocephalus
Canadian institutionsNational Research Council Canada
FundersNational Research Council Canada
KeywordsVentriculostomyThird ventricleComputer scienceCraniotomySimulationEndoscopic third ventriculostomyHaptic technologyNeurosurgeryHydrocephalusMedicineSurgery

Abstract

fetched live from OpenAlex

Endoscopic third ventriculostomy is a procedure used to treat hydrocephalus by making a perforation in the floor of the third ventricle of the brain under endoscopic guidance. We report on our initial experience in developing an endoscopic third ventriculostomy simulator for neurosurgery residents, including the definition of the simulation content and integration on NeuroTouch, a simulator for microneurosurgery training. The simulator includes exercises in which the trainee is asked to choose the location of the burr hole and the orientation of the trajectory to the foramen of Monro, or is required to find the third ventricle with a neuro-endoscope and perforate its floor. The simulator provides feedback on trainee performance either graphically or using quantitative metrics. The simulator allows easily switching from endoscopic third ventriculostomy mode to microscopic craniotomy mode using a retractable stereoscope based on a single screen and mirrors, detachable plastic heads, and quick-connect tool handles to give more realistic haptic feedback.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.025
GPT teacher head0.272
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2013
Admission routes2
Has abstractyes

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