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Record W2004616153 · doi:10.1109/ccece.2013.6567771

Temporal bone surgical simulation employing a multicore architecture

2013· article· en· W2004616153 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComputer scienceHaptic technologySmoothingMulti-core processorSimulationVoxelArtificial intelligenceComputer visionParallel computing

Abstract

fetched live from OpenAlex

This paper presents a novel haptic temporal bone surgery simulator. Temporal bone surgery is challenging as anatomic structures are complex and encased within bone. The new simulator is designed to train surgical residents permitting virtual practice of complex patient specific procedures before actual surgery is performed. The simulation employs voxels for collision detection and bone removal. It is designed to run on multicore computers that allow it to render changes in the temporal bone using Marching Cubes and Laplacian HC smoothing, in real time. This approach allows the use of consumer level hardware to display the simulation in stereo 3D, and serves as a vehicle for several new training tools, now incorporated into the simulation. Future plans include trials of validity, efficacy and cost effective integration into training and surgical rehearsal.

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.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.897
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.021
GPT teacher head0.279
Teacher spread0.257 · 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

Quick stats

Citations9
Published2013
Admission routes1
Has abstractyes

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