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Record W2162378983 · doi:10.1109/embc.2013.6610022

An optically coupled system for quantitative monitoring of MRI gradient currents induced into endocardial leads

2013· article· en· W2162378983 on OpenAlexfundno aff
Eugenio Mattei, Giovanni Calcagnini, Michele Triventi, A. Delogu, Miguel Del Guercio, Antonio Angeloni, P. Bartolini

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsnot available
FundersCanadian Patient Safety InstituteBroadcom Foundation
KeywordsPhotodiodeTransmitterSIGNAL (programming language)Magnetic resonance imagingCurrent (fluid)Electromagnetic coilOptical fiberMicrocontrollerMaterials scienceDiodePhysicsOptoelectronicsBiomedical engineeringElectrical engineeringComputer scienceOpticsEngineeringMedicine

Abstract

fetched live from OpenAlex

The time-varying gradient fields generated during Magnetic Resonance Imaging (MRI) procedures have the potential to induce electrical current on implanted endocardial leads. Whether this current can result in undesired cardiac stimulation is unknown. This paper presents an optically coupled system with the potential to quantitatively measure the currents induced by the gradient fields into endocardial leads during MRI procedures. Our system is based on a microcontroller that works as analog-to-digital (A/D) converter and sends the current signal acquired from the lead to an optical high-speed light-emitting-diode transmitter. Plastic fiber guides the light outside the MRI chamber, to a photodiode receiver and then to an acquisition board connected to a PC. The preliminary characterization of the performances of the system is also 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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.045
GPT teacher head0.388
Teacher spread0.343 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations3
Published2013
Admission routes1
Has abstractyes

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