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Record W7020074080

Introducing 1,5 T MRgFUS

2015· book-chapter· en· W7020074080 on OpenAlexfundno aff

Bibliographic record

VenueNova Science Publishers (Nova Science Publishers, Inc.) · 2015
Typebook-chapter
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsnot available
FundersSchool of Medicine, University of California, IrvineMilitary Health SystemWalter Reed National Military Medical CenterDefense and Veterans Brain Injury CenterUniversity of California, IrvineUniversity of California, Los AngelesUniversity of WaterlooGeorgetown UniversityKaiser Permanente
KeywordsModalitiesTheme (computing)ConventionWorld classSociology of scientific knowledgeClass (philosophy)
DOInot available

Abstract

fetched live from OpenAlex

MRgFUS is a new noninvasive thermal ablation method, consisting of a high focused ultrasound beam and a MR scanner for imaging and real-time thermal mapping. For decades, therapeutic transcranial ultrasound (tcMRgFUS) was assumed impossible. Novel technology using high-power phased array transducers and multiple channel driving electronics enabled a sharp focal point in the planned target through the intact skull allowing the use of this innovative technology for functional neurosurgery. Preliminary results have been published with systems operating with 3T MR units on patients with neurologic disorders such as essential tremor, tremor dominant idiopathic Parkinson's disease and neuropathic pain. In this talk the preliminary results achieved with the world-first tcMRgFUS system operating with a 1,5T MR (first Italian tcMRgFUS installation ever) will be presented.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.006

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 head0.041
GPT teacher head0.260
Teacher spread0.219 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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