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Record W2159894132 · doi:10.1109/tbme.2004.834251

Peripheral Nerve Stimulation by Gradient Switching Fields in Magnetic Resonance Imaging

2004· article· en· W2159894132 on OpenAlex
P.P.M. So, M.A. Stuchly, J.A. Nyenhuis

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 Transactions on Biomedical Engineering · 2004
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMagnetic resonance imagingTorsoBiomagnetismElectromagnetic coilCoronal planeMagnetic fieldNuclear magnetic resonancePhysicsBiomedical engineeringMaterials scienceStimulationMagnetic resonance neurographyAnatomyMedicineRadiology

Abstract

fetched live from OpenAlex

A heterogeneous model of the human body and the scalar potential finite difference method are used to compute electric fields induced in tissue by magnetic field exposures. Two types of coils are considered that simulate exposure to gradient switching fields during magnetic resonance imaging (MRI). These coils producing coronal (y axis) and axial (z axis) magnetic fields have previously been used in experiments with humans. The computed fields can, therefore, be directly compared to human response data. The computed electric fields in subcutaneous fat and skin corresponding to peripheral nerve stimulation (PNS) thresholds in humans in simulated MRI experiments range from 3.8 to 5.8 V/m for the fields exceeded in 0.5% of tissue volume (skin and fat of the torso). The threshold depends on coil type and position along the body, and on the anatomy and resolution of the human body model. The computed values are in agreement with previously established thresholds for neural stimulation.

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 score0.903
Threshold uncertainty score0.541

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 head0.006
GPT teacher head0.251
Teacher spread0.245 · 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