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Record W2884341219 · doi:10.1055/s-0038-1633607

Learning Curve of a Virtual Reality Simulator (Neurotouch) for Endoscopic Sinus Surgery

2018· article· en· W2884341219 on OpenAlexaff
Mirko Kolarski, Christopher M. K. L. Yao, Stephen Chen, Eric Monteiro, Allan Vescan

Bibliographic record

VenueJournal of Neurological Surgery Part B Skull Base · 2018
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHaptic technologyVirtual realityLearning curveComputer scienceCurriculumFidelityEndoscopic sinus surgeryCadaveric spasmSimulationMedical physicsHuman–computer interactionSurgeryMedicineMultimediaPsychology

Abstract

fetched live from OpenAlex

Background Endoscopic sinus surgery is nuanced and technically challenging for novice trainees. With modern resident duty limitations and an increased focus on quality metrics in education and beyond, it is important to develop alternative tools for teaching this skill. Neurotouch is a validated high-fidelity virtual-reality simulator that provides haptic feedback and visual cues to simulate sinus surgery. Implementing the simulator in the era of competency-based curriculum has not been thoroughly investigated. In this study, we determined the learning curve for three tasks on the Neurotouch and assessed the Neurotouch as a learning tool compared with standard practice during cadaveric endoscopic sinus surgery.

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)0.002
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.101
GPT teacher head0.331
Teacher spread0.230 · 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.

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

Citations2
Published2018
Admission routes1
Has abstractyes

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