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Record W2295427581 · doi:10.3348/kjr.2016.17.3.307

Physiologic Assessment of Coronary Artery Disease: Focus on Fractional Flow Reserve

2016· review· en· W2295427581 on OpenAlexaff
Doyeon Hwang, Joo Myung Lee, Bon‐Kwon Koo

Bibliographic record

VenueKorean Journal of Radiology · 2016
Typereview
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsInstitute of Aging
FundersSt. Jude Medical
KeywordsFractional flow reserveMedicineCardiologyRevascularizationCoronary artery diseaseInternal medicineStenosisGold standard (test)RadiologyIschemiaAngiographyCoronary angiographyDiseaseMyocardial infarction

Abstract

fetched live from OpenAlex

The presence of myocardial ischemia is the most important prognostic factor in patients with ischemic heart disease. Fractional flow reserve (FFR) is a gold standard invasive method used to detect the stenosis-specific myocardial ischemia. FFR-guided revascularization strategy is superior to angiography-guided strategy. The recently developed hyperemia-free index, instantaneous wave free ratio is being actively investigated. A non-invasive FFR derived from coronary CT angiography is now used in clinical practice. Due to rapid expansion of invasive and non-invasive physiologic assessment, comprehensive understanding of the role and potential pitfalls of each modality are required for its application. In this review, we focus on the basic and clinical aspects of physiologic assessment in ischemic heart 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 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)0.002
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.053
GPT teacher head0.378
Teacher spread0.325 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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