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Record W2937060919 · doi:10.1253/circj.cj-18-1269

Clinical Impact of Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve on Japanese Population in the ADVANCE Registry

2019· article· en· W2937060919 on OpenAlexaff
Yasutsugu Shiono, Hitoshi Matsuo, Tomohiro Kawasaki, Tetsuya Amano, Hironori Kitabata, Takashi Kubo, Yoshihiro Morino, Shunichi Yoda, Tomohiro Sakamoto, Hiroshi Ito, Junya Shite, Hiromasa Otake, Nobuhiro Tanaka, Mitsuyasu Terashima, Kazushige Kadota, Manesh R. Patel, Koen Nieman, Campbell Rogers, Bjarne Linde Nørgaard, Jeroen J. Bax, Gilbert Raff, Kavitha M. Chinnaiyan, Daniel S. Berman, Timothy Fairbairn, Lynne Koweek, Jonathon Leipsic, Takashi Akasaka

Bibliographic record

VenueCirculation Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversity of British Columbia
FundersAbbott VascularDuke Clinical Research InstituteBoston Scientific JapanVerily Life SciencesSiemens HealthineersDaiichi-SankyoBoston Scientific Corporation
KeywordsFractional flow reserveMedicineCoronary angiographyComputed tomographyAngiographyPopulationCardiologyComputed tomography angiographyRadiologyInternal medicineNuclear medicineMyocardial infarctionEnvironmental health

Abstract

fetched live from OpenAlex

Background:Coronary computed tomography angiography (cCTA)-derived fractional flow reserve (FFRCT) is a promising diagnostic method for the evaluation of coronary artery disease (CAD). However, clinical data regarding FFRCTin Japan are scarce, so we assessed the clinical impact of using FFRCTin a Japanese population.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.346
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 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

Citations16
Published2019
Admission routes1
Has abstractyes

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