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Record W2515578666 · doi:10.1002/rcs.1760

An image‐guided automated robot for MRI breast biopsy

2016· article· en· W2515578666 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

VenueInternational Journal of Medical Robotics and Computer Assisted Surgery · 2016
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsAir CanadaMcMaster UniversityCanadian Society of Pharmacology and Therapeutics
Fundersnot available
KeywordsRepeatabilityComputer scienceBreast biopsyComputer visionArtificial intelligenceImage qualityRobotProjectileSimulationMedicineImage (mathematics)MammographyBreast cancerMaterials scienceMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: The IGAR (Image-guided Automated Robot) is a robotic platform capable of performing highly accurate clinical interventions under image guidance. The IGAR is unique in that it demonstrates MRI compatibility and maintains safe operation, adequate shielding, high image quality, and accurate robotic control even while in an imaging environment. The IGAR is initially intended for breast biopsy. METHODS: Tests for projectile hazards, heating, signal-to-noise ratio loss, and geometric distortion were used to demonstrate MR compatibility. Accuracy and repeatability of the robotic system were tested on benchtop models to establish a baseline of precision. RESULTS: The IGAR averaged an accuracy of 0.34 mm and a repeatability of 0.2 mm. There was no significant distortion attributable to the robot, no projectile risk, and no unacceptable levels of heating. CONCLUSION: The IGAR system is safe and effective in an MRI environment Copyright © 2016 John Wiley & Sons, Ltd.

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)0.001
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 score0.888
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.019
GPT teacher head0.289
Teacher spread0.270 · 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