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Update on the clinical utility of coronary computed tomographic angiography in stable angina pectoris

2017· review· en· W2972206885 on OpenAlexaff
Shaw Hua Kueh, Christopher Naoum

Bibliographic record

VenueMinerva Cardiology and Angiology · 2017
Typereview
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsSt. Paul's Hospital
Fundersnot available
KeywordsMedicineCoronary artery diseaseComputed tomographic angiographyRadiologyCADChest painCardiologyAnginaStable anginaComputed tomographicInternal medicineClinical trialUnstable anginaAngiographyComputed tomographyCoronary heart diseaseMyocardial infarction

Abstract

fetched live from OpenAlex

Over the last decade, coronary computed tomographic angiography (CCTA) has emerged as a valuable non-invasive imaging modality with excellent diagnostic performance compared to invasive coronary angiography (ICA) for identifying patients with coronary artery disease (CAD). Beyond the diagnosis of CAD, CCTA also provides valuable prognostic information. While patients with normal CCTA have excellent long-term prognosis, among those with CAD, increasing CAD extent and severity is associated with increased cardiovascular event risk over both medium- and long-term follow-up in both men and women. The ability to image non-obstructive CAD is a particularly unique attribute of CCTA. Moreover, the ability to assess plaque features on CCTA has further enhanced our understanding of coronary plaque dynamics and the prediction of future cardiovascular events. The clinical impact of CCTA has been recently evaluated in two landmark prospective multicenter trials, which have provided insights into the influence of CCTA on the clinical management of symptomatic patients with suspected CAD. We review the value of CCTA in the evaluation of patients with stable chest pain including its diagnostic performance, prognostic utility and real-world clinical application.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.641
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.121
GPT teacher head0.392
Teacher spread0.272 · 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.

Study designObservational
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

Citations1
Published2017
Admission routes1
Has abstractyes

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