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Record W2160030138 · doi:10.1109/tmech.2008.924118

Human–Machine Interface for Robotic Surgery and Stereotaxy

2008· article· en· W2160030138 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

VenueIEEE/ASME Transactions on Mechatronics · 2008
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsFoothills Medical CentreUniversity of Calgary
Fundersnot available
KeywordsWorkstationHaptic technologyStereoscopyInterface (matter)Computer scienceHuman–computer interactionRobotTeleroboticsComponent (thermodynamics)TeleoperationArtificial intelligenceEmbedded systemOperating systemMobile robot

Abstract

fetched live from OpenAlex

While considerable technology has been integrated into the operating room, until recently, the actual performance of surgery has seen relatively few changes, relying mainly on hand-eye coordination. This paper outlines the development and composition as well as the requirements and reasoning that lead to the human-machine interface on neuroArm, a telerobotic surgical system. A critical component of the system was the workstation, where information was provided to and received from the operator. The surgeon controls the robotic system using two force-feedback hand controllers based on visual information from a stereoscopic viewing device and two liquid crystal displays. Two touch screens allow the user to monitor and control the settings of the robot and to view and manipulate 3-D MR images. Audio feedback from the surgical site and the operating room staff is also provided by a wireless communication system. The workstation components were chosen not only to recreate the sight, sound, and touch of surgery but also to facilitate the integration of surgeons with advanced imaging and robotic technologies.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score0.856

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 head0.030
GPT teacher head0.246
Teacher spread0.216 · 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