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Record W4206760462 · doi:10.1002/rcs.2364

Robotic system for top to bottom MRgFUS therapy of multiple cancer types

2022· article· en· W4206760462 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 · 2022
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsMagnetic resonance imagingImaging phantomBiomedical engineeringSupine positionComputer scienceUltrasoundMaterials scienceTransducerMedicineRadiologyPhysicsAcousticsSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: A robotic system for Magnetic Resonance guided Focussed Ultrasound (MRgFUS) therapy of tumours in the breast, bone, thyroid, and abdomen was developed. METHODS: A special C-shaped structure was designed to be attached to the table of conventional magnetic resonance imaging (MRI) systems carrying 4 computer-controlled motion stages dedicated to positioning a 2.75 MHz spherically focussed transducer relative to a patient placed in the supine position. The developed system was evaluated for its MRI compatibility and heating abilities in agar-based phantoms and freshly excised tissue. RESULTS: Compatibility of the system with a clinical high-field MRI scanner was demonstrated. FUS heating in the phantom was successfully monitored by magnetic resonance thermometry without any evidence of magnetically induced phenomena. Cigar-shaped discrete lesions and well-defined areas of overlapping lesions were inflicted in excised tissue by robotic movement along grid patterns. CONCLUSIONS: The developed MRgFUS robotic system was proven safe and efficient by ex-vivo feasibility studies.

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 score0.642
Threshold uncertainty score0.268

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 head0.023
GPT teacher head0.264
Teacher spread0.241 · 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