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Record W2954279339 · doi:10.3171/2019.4.jns183184

Development and evaluation of a patient-specific surgical simulator for endoscopic colloid cyst resection

2019· article· en· W2954279339 on OpenAlexaff
Vivek Bodani, Gerben E. Breimer, Faizal Haji, Thomas Looi, James M. Drake

Bibliographic record

VenueJournal of neurosurgery · 2019
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsWestern UniversitySickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsColloid cystMedicineThird ventricleHydrocephalusRadiologyNeuronavigationCystSurgeryMagnetic resonance imagingAnatomy

Abstract

fetched live from OpenAlex

OBJECTIVE: Endoscopic resection of third-ventricle colloid cysts is technically challenging due to the limited dexterity and visualization provided by neuroendoscopic instruments. Extensive training and experience are required to master the learning curve. To improve the education of neurosurgical trainees in this procedure, a synthetic surgical simulator was developed and its realism, procedural content, and utility as a training instrument were evaluated. METHODS: The simulator was developed based on the neuroimaging (axial noncontrast CT and T1-weighted gadolinium-enhanced MRI) of an 8-year-old patient with a colloid cyst and hydrocephalus. Image segmentation, computer-aided design, rapid prototyping (3D printing), and silicone molding techniques were used to produce models of the skull, brain, ventricles, and colloid cyst. The cyst was filled with a viscous fluid and secured to the roof of the third ventricle. The choroid plexus and intraventricular veins were also included. Twenty-four neurosurgical trainees performed a simulated colloid cyst resection using a 30° angled endoscope, neuroendoscopic instruments, and image guidance. Using a 19-item feedback survey (5-point Likert scales), participants evaluated the simulator across 5 domains: anatomy, instrument handling, procedural content, perceived realism, and confidence and comfort level. RESULTS: Participants found the simulator's anatomy to be highly realistic (mean 4.34 ± 0.63 [SD]) and appreciated the use of actual instruments (mean 4.38 ± 0.58). The procedural content was also rated highly (mean 4.28 ± 0.77); however, the perceived realism was rated slightly lower (mean 4.08 ± 0.63). Participants reported greater confidence in their ability to perform an endoscopic colloid cyst resection after using the simulator (mean 4.45 ± 0.68). Twenty-three participants (95.8%) indicated that they would use the simulator for additional training. Recommendations were made to develop complex case scenarios for experienced trainees (normal-sized ventricles, choroid plexus adherent to cyst wall, bleeding scenarios) and incorporate advanced instrumentation such as side-cutting aspiration devices. CONCLUSIONS: A patient-specific synthetic surgical simulator for training residents and fellows in endoscopic colloid cyst resection was successfully developed. The simulator's anatomy, instrument handling, and procedural content were found to be realistic. The simulator may serve as a valuable educational tool to learn the critical steps of endoscopic colloid cyst resection, develop a detailed understanding of intraventricular anatomy, and gain proficiency with bimanual neuroendoscopic techniques.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.066
GPT teacher head0.328
Teacher spread0.262 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations19
Published2019
Admission routes1
Has abstractyes

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