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Record W3041870945 · doi:10.1097/hco.0000000000000776

Stress cardiac MRI in stable coronary artery disease

2020· review· en· W3041870945 on OpenAlexaff
Yin Ge, Djeven P. Deva, Kim A. Connelly, Andrew T. Yan

Bibliographic record

VenueCurrent Opinion in Cardiology · 2020
Typereview
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsCoronary artery diseaseMedicineFractional flow reserveCADStress testing (software)Coronary angiographyCardiologyRadiologyCardiac magnetic resonanceInternal medicineStress EchocardiographyMagnetic resonance imagingAngiographyCardiac magnetic resonance imagingMyocardial infarction

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Non-invasive testing is often the first step in the evaluation of stable coronary artery disease (CAD). Stress cardiac magnetic resonance imaging (CMR) is an established modality with high diagnostic accuracy and prognostic value. This review will focus on the recent advances in understanding how stress CMR can help guide patient care. RECENT FINDINGS: Diagnostic accuracy of stress CMR has been validated against coronary angiography with fractional flow reserve (FFR) in patients with stable CAD. Large registry data have shown stress CMR to have important prognostic importance and that its cost-effectiveness compares favorably to alternatives. In patients with stable CAD, guidance using a CMR based strategy led to equivalent outcomes when compared to coronary angiography with FFR. SUMMARY: In persons with stable CAD, Stress CMR is an accurate and cost-effective imaging modality that should be considered in patients at intermediate pre-test probability of CAD. Prognostic studies have shown it to have excellent negative predictive value and that it can safely serve as a "gatekeeper" for invasive angiography.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.895
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
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 head0.079
GPT teacher head0.387
Teacher spread0.307 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2020
Admission routes1
Has abstractyes

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