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AN IMAGE-GUIDED MAGNETIC RESONANCE-COMPATIBLE SURGICAL ROBOT

2008· article· en· W2094882175 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

VenueNeurosurgery · 2008
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRoboticsMedicineArtificial intelligenceMagnetic resonance imagingRobotComputer visionMicrosurgeryStereotaxyRobotic surgeryMedical physicsHaptic technologySurgeryComputer scienceRadiology

Abstract

fetched live from OpenAlex

OBJECTIVE: The past decade has witnessed the increasing application of robotics in surgery, yet there is no existing system that combines stereotaxy and microsurgery in an imaging environment. To fulfill this niche, we have designed and manufactured an image-guided robotic system that is compatible with magnetic resonance imaging. METHODS: The system conveys the sight, touch, and sound of surgery to an operator seated at a remote workstation. Motion scaling, tremor filtering, and precision robotics allow surgeons to rapidly attain technical proficiency while working at a spatial resolution of 50 to 100 microm instead of a few millimeters. This system has the potential to shift surgery from the organ toward the cellular level. RESULTS: By integrating the robot with images obtained during the procedure, the effects of surgery on both the lesion and brain are immediately revealed. CONCLUSION: We are providing technology to advance and transform surgery with the potential to improve patient outcome.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.605

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 head0.028
GPT teacher head0.246
Teacher spread0.218 · 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