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Record W2532184202 · doi:10.1109/have.2004.1391889

Haptic tele-surgery simulation

2005· article· en· W2532184202 on OpenAlex
Jian Zhou, Xiaojun Shen, N.D. Georganas

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
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHaptic technologyTrainerMedical simulationComputer scienceArchitectureSurgical simulationSimulationHuman–computer interactionMedicineSurgery

Abstract

fetched live from OpenAlex

In the last decades, with the rapid development of electronic and computer technologies, advanced medical equipment has been introduced to facilitate medical diagnosis and treatment. Yet, medical education and training approaches have not benefited much from these developments. As the kinds of surgical operations increase, specialized procedures become more and more sophisticated. Thus, the medical specialists have to be well-trained before they perform surgery on real human beings. The purpose of this research is to develop a collaborative haptic simulation architecture for tracheotomy surgery and its training program. In this simulation, there will be two scenarios. In the first, two doctors operate collaboratively from different locations. In the second scenario, the trainer coaches the trainee on how to perform the surgery successfully in a tele-mentoring manner.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score1.000

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)0.0010.001

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 head0.020
GPT teacher head0.222
Teacher spread0.202 · 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

Citations23
Published2005
Admission routes1
Has abstractyes

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