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Record W2926578282 · doi:10.1530/erp-18-0068

Stress echocardiography in coronary artery disease: a practical guideline from the British Society of Echocardiography

2019· article· en· W2926578282 on OpenAlexaboutno aff
Richard P. Steeds, Richard Wheeler, Sanjeev Bhattacharyya, Joseph Reiken, Petros Nihoyannopoulos, Roxy Senior, Mark Monaghan, Vishal Sharma

Bibliographic record

VenueEcho Research and Practice · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineChest painGuidelineCardiologyCoronary artery diseaseInternal medicineStress EchocardiographyIschaemic heart diseaseAnginaCanadian Cardiovascular SocietyRadiologyDiseaseMyocardial infarctionPathology

Abstract

fetched live from OpenAlex

Stress echocardiography is an established technique for assessing coronary artery disease. It has primarily been used for the diagnosis and assessment of patients presenting with chest pain in whom there is an intermediate probability of coronary artery disease. In addition, it is used for risk stratification and to guide revascularisation in patients with known ischaemic heart disease. Although cardiac computed tomography has recently been recommended in the United Kingdom as the first-line investigation in patients presenting for the first time with atypical or typical angina, stress echocardiography continues to have an important role in the assessment of patients with lesions of uncertain functional significance and patients with known ischaemic heart disease who represent with chest pain. In this guideline from the British Society of Echocardiography, the indications and recommended protocols are outlined for the assessment of ischaemic heart disease by stress echocardiography.

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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.103
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.388
Teacher spread0.345 · 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 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

Citations43
Published2019
Admission routes1
Has abstractyes

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