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Record W2140800188 · doi:10.1016/j.jacc.2013.07.076

Anatomic Versus Physiologic Assessment of Coronary Artery Disease

2013· review· en· W2140800188 on OpenAlexafffund
K. Lance Gould, Nils P. Johnson, Timothy M. Bateman, Rob Beanlands, Frank M. Bengel, Robert Bober, Paolo G. Camici, Manuel D. Cerqueira, Benjamin J.W. Chow, Marcelo F. Di Carli, Sharmila Dorbala, Henry Gewirtz, Robert J. Gropler, Philipp A. Kaufmann, Paul Knaapen, Juhani Knuuti, Michael E. Merhige, K.Peter Rentrop, Terrence D. Ruddy, Heinrich R. Schelbert, Thomas H. Schindler, Markus Schwaiger, Stefano Sdringola, John Vitarello, Kim A. Williams, Donald Gordon, Vasken Dilsizian, Jagat Narula

Bibliographic record

VenueJournal of the American College of Cardiology · 2013
Typereview
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversity of Ottawa
FundersAstellas PharmaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungGE HealthcareGenentechHeart and Stroke Foundation of Canada
KeywordsMedicineCoronary artery diseaseFractional flow reserveCardiologyRevascularizationCoronary flow reserveInternal medicineStenosisArteryPositron emission tomographyRadiologyCoronary angiographyMyocardial infarction

Abstract

fetched live from OpenAlex

Angiographic severity of coronary artery stenosis has historically been the primary guide to revascularization or medical management of coronary artery disease. However, physiologic severity defined by coronary pressure and/or flow has resurged into clinical prominence as a potential, fundamental change from anatomically to physiologically guided management. This review addresses clinical coronary physiology-pressure and flow-as clinical tools for treating patients. We clarify the basic concepts that hold true for whatever technology measures coronary physiology directly and reliably, here focusing on positron emission tomography and its interplay with intracoronary measurements.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.035
GPT teacher head0.358
Teacher spread0.323 · 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 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

Citations564
Published2013
Admission routes2
Has abstractyes

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