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Record W2035720245 · doi:10.1016/j.carj.2014.08.001

Canadian Heart Rhythm Society and Canadian Association of Radiologists Consensus Statement on Magnetic Resonance Imaging with Cardiac Implantable Electronic Devices

2014· article· en· W2035720245 on OpenAlexaffabout
Atul Verma, Andrew C.T. Ha, Carole Dennie, Vidal Essebag, Derek V. Exner, Naeem Khan, Chris Lane, Jonathan Leipsic, François Philippon, Marcos Loreto Sampaio, Nicola Schieda, Colette Seifer, Alain Berthiaume, Debra Campbell, Santanu Chakraborty

Bibliographic record

VenueCanadian Association of Radiologists Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsSt. Boniface HospitalInstitut universitaire de cardiologie et de pneumologie de QuébecIzaak Walton Killam Health CentreSt. Paul's HospitalUniversity Health NetworkOttawa HospitalLibin Cardiovascular Institute of AlbertaKingston General HospitalRoyal Jubilee HospitalMcGill University Health CentreMcGill UniversitySouthlake Regional Health Center
Fundersnot available
KeywordsMedicineMagnetic resonance imagingStatement (logic)Heart RhythmMedical physicsCardiac magnetic resonanceRadiologyMedical emergencyCardiology

Abstract

fetched live from OpenAlex

Magnetic resonance imaging (MRI) has historically been considered contraindicated for individuals with cardiac implantable electronic devices (CIEDs) such as pacemakers and implantable defibrillators. Magnetic resonance scanners produce magnetic fields that can interact negatively with the metallic components of CIEDs. However, as CIED technology has advanced, newer MRI conditional devices have been developed that are now in clinical use and these systems have had demonstrated safety in the MRI environment. Despite the supportive data of such CIED systems, physicians remain reluctant to perform MRI scanning of conditional devices. This joint statement by the Canadian Heart Rhythm Society and the Canadian Association of Radiologists describes a collaborative process by which CIED specialists and clinics can work with radiology departments and specialists to safely perform MRI in patients with MRI conditional CIED systems. The steps required for patient and scanning preparation and the roles and responsibilities of the CIED and radiology departments are outlined. We also briefly outline the risks and a process by which patients with nonconditional CIEDs might also receive MRI in highly specialized centres. This document supports MRI in patients with MRI conditional CIEDs and offers recommendations on how this can be implemented safely and effectively.

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 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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.250
Teacher spread0.244 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations22
Published2014
Admission routes2
Has abstractyes

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