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Record W2071324500 · doi:10.1586/14779072.2013.837755

Integrating anatomical and functional imaging for the assessment of coronary artery disease

2013· review· en· W2071324500 on OpenAlexaff
Andrea K.Y. Lee, Mohammed A. Qutub, Ahmed Aljizeeri, Benjamin J.W. Chow

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2013
Typereview
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversity of OttawaUniversity of British Columbia
Fundersnot available
KeywordsMedicineCoronary artery diseaseFractional flow reserveCADRadiologyCardiologyFunctional imagingRisk stratificationCoronary angiographyCardiac imagingAngiographyModality (human–computer interaction)Internal medicineMyocardial infarctionArtificial intelligence

Abstract

fetched live from OpenAlex

Coronary artery disease (CAD) is a leading cause of morbidity and mortality. Invasive cardiac angiography with fractional flow reserve measurement allows for the anatomical and functional assessment of CAD. Given the invasive nature of invasive cardiac angiography and the risks of procedure-related complications, research has focused upon noninvasive methods for anatomical and functional measures of CAD. As such, there is growing interest in the development of hybrid imaging because it may provide incremental diagnostic information over each imaging modality alone. We will provide an overview of the evidence to date on the anatomical and functional stratification of CAD and current hybrid techniques.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.945
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.005
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 head0.037
GPT teacher head0.366
Teacher spread0.328 · 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 designOther design
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

Citations3
Published2013
Admission routes1
Has abstractyes

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