MétaCan
Menu
Back to cohort
Record W2989976879 · doi:10.1148/ryct.2019190050

Coronary CT Angiography-derived Fractional Flow Reserve Testing in Patients with Stable Coronary Artery Disease: Recommendations on Interpretation and Reporting

2019· article· en· W2989976879 on OpenAlexaff
Bjarne Linde Nørgaard, Timothy Fairbairn, Robert D. Safian, Mark Rabbat, Brian Ko, Jesper Møller Jensen, Koen Nieman, Kavitha M. Chinnaiyan, Niels Peter Rønnow Sand, Hitoshi Matsuo, Jonathon Leipsic, Gilbert Raff

Bibliographic record

VenueRadiology Cardiothoracic Imaging · 2019
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsSt. Paul's Hospital
Fundersnot available
KeywordsFractional flow reserveMedicineCoronary artery diseaseCoronary angiographyCardiologyInternal medicineAngiographyRadiologyMyocardial infarction

Abstract

fetched live from OpenAlex

Noninvasive fractional flow reserve derived from coronary CT angiography (FFRCT) is increasingly used in patients with coronary artery disease as a gatekeeper to the catheterization laboratory. While there is emerging evidence of the clinical benefit of FFRCT in patients with moderate coronary disease as determined with coronary CT angiography, there has been less focus on interpretation, reporting, and integration of FFRCT results into routine clinical practice. Because FFRCT analysis provides a plethora of information regarding pressure and flow across the entire coronary tree, standardized criteria on interpretation and reporting of the FFRCT analysis result are of crucial importance both in context of the clinical adoption and in future research. This report represents expert opinion and recommendation on a standardized FFRCT interpretation and reporting approach. Keywords: Adults, CT-Angiography, Coronary Arteries, Fractional Flow Reserve, Heart Published under a CC BY 4.0 license.

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)0.001
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 score0.033
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.018
GPT teacher head0.293
Teacher spread0.275 · 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

Citations120
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueRadiology Cardiothoracic ImagingSame topicCoronary Interventions and DiagnosticsFrench-language works237,207