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Record W2951539836 · doi:10.1056/nejmoa1716734

Magnetic Resonance Perfusion or Fractional Flow Reserve in Coronary Disease

2019· article· en· W2951539836 on OpenAlexfundaboutno aff
Eike Nagel, John P. Greenwood, Gerry P McCann, Nuno Bettencourt, Ajay M. Shah, Shazia Hussain, Divaka Perera, Sven Plein, Chiara Bucciarelli‐Ducci, Matthias Paul, Mark Westwood, Michael Marber, Wolf-Stefan Richter, Valentina O. Püntmann, Carsten Schwenke, Jeanette Schulz‐Menger, Rajiv Das, Joyce Wong, Derek J. Hausenloy, Henning Steen, Colin Berry

Bibliographic record

VenueNew England Journal of Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsnot available
FundersGuy's and St Thomas' NHS Foundation TrustAbbott VascularCircle Cardiovascular ImagingKing's Health PartnersNational Institute on Disability and Rehabilitation ResearchUniversity of GlasgowSiemens HealthineersKing's College LondonUniversity College LondonBritish Heart FoundationNational Institute for Health and Care ResearchDeutsches Zentrum für Herz-KreislaufforschungSouth London and Maudsley NHS Foundation TrustBayerAstraZeneca
KeywordsFractional flow reserveMedicineRevascularizationCardiologyInternal medicineAnginaMyocardial infarctionMagnetic resonance imagingCanadian Cardiovascular SocietyCoronary artery diseaseMyocardial perfusion imagingUnstable anginaRadiologyCoronary angiography

Abstract

fetched live from OpenAlex

BACKGROUND: In patients with stable angina, two strategies are often used to guide revascularization: one involves myocardial-perfusion cardiovascular magnetic resonance imaging (MRI), and the other involves invasive angiography and measurement of fractional flow reserve (FFR). Whether a cardiovascular MRI-based strategy is noninferior to an FFR-based strategy with respect to major adverse cardiac events has not been established. METHODS: We performed an unblinded, multicenter, clinical-effectiveness trial by randomly assigning 918 patients with typical angina and either two or more cardiovascular risk factors or a positive exercise treadmill test to a cardiovascular MRI-based strategy or an FFR-based strategy. Revascularization was recommended for patients in the cardiovascular-MRI group with ischemia in at least 6% of the myocardium or in the FFR group with an FFR of 0.8 or less. The composite primary outcome was death, nonfatal myocardial infarction, or target-vessel revascularization within 1 year. The noninferiority margin was a risk difference of 6 percentage points. RESULTS: A total of 184 of 454 patients (40.5%) in the cardiovascular-MRI group and 213 of 464 patients (45.9%) in the FFR group met criteria to recommend revascularization (P = 0.11). Fewer patients in the cardiovascular-MRI group than in the FFR group underwent index revascularization (162 [35.7%] vs. 209 [45.0%], P = 0.005). The primary outcome occurred in 15 of 421 patients (3.6%) in the cardiovascular-MRI group and 16 of 430 patients (3.7%) in the FFR group (risk difference, -0.2 percentage points; 95% confidence interval, -2.7 to 2.4), findings that met the noninferiority threshold. The percentage of patients free from angina at 12 months did not differ significantly between the two groups (49.2% in the cardiovascular-MRI group and 43.8% in the FFR group, P = 0.21). CONCLUSIONS: Among patients with stable angina and risk factors for coronary artery disease, myocardial-perfusion cardiovascular MRI was associated with a lower incidence of coronary revascularization than FFR and was noninferior to FFR with respect to major adverse cardiac events. (Funded by the Guy's and St. Thomas' Biomedical Research Centre of the National Institute for Health Research and others; MR-INFORM ClinicalTrials.gov number, NCT01236807.).

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)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.280
Teacher spread0.266 · 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 designRandomized trial
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

Citations479
Published2019
Admission routes2
Has abstractyes

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