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Record W2132987297 · doi:10.1002/mrm.20699

Comparison of X‐ray fluoroscopy and interventional magnetic resonance imaging for the assessment of coronary artery stenoses in swine

2005· article· en· W2132987297 on OpenAlexaff
Jordin D. Green, Reed A. Omary, Brian E. Schirf, Richard Tang, Biao Lu, James A. Gehl, Jonathan Huang, James Carr, F. Scott Pereles, Debiao Li

Bibliographic record

VenueMagnetic Resonance in Medicine · 2005
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsSiemens (Canada)
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood Institute
KeywordsMedicineStenosisRadiologyMagnetic resonance angiographyFluoroscopyIntraclass correlationCoronary artery diseaseMagnetic resonance imagingAngiographyCatheterNuclear medicineInternal medicine

Abstract

fetched live from OpenAlex

The accuracy of a two-step interventional MRI protocol to quantify coronary artery disease was compared to the clinical gold standard, X-ray angiography. Studies were conducted in nine swine with a surgically induced stenosis in the proximal left circumflex coronary artery. The two-step protocol consisted of catheter-directed magnetic resonance angiography (MRA), which was first used to localize the stenosis, followed by MRI cross-sectional images to quantify the degree of stenosis without the use of contrast agent. Line signal intensity profiles were drawn across the vessel diameter at the stenosis site and proximal to the stenosis for each data set to measure percentage stenosis for each animal. Catheter-directed MRA successfully detected eight of nine stenoses. Cross-sectional MRI accurately quantified each stenosis, with strong agreement to the measurements made using X-ray fluoroscopy (intraclass correlation coefficient = 0.955; P < 0.05). This study demonstrates that in the future interventional MRI may be an alternative to X-ray angiography for the detection and quantification of coronary artery disease.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.024
GPT teacher head0.363
Teacher spread0.339 · 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

Citations17
Published2005
Admission routes1
Has abstractyes

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