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Record W2747655903 · doi:10.4244/eij-d-17-00463

State of the art: optimal medical therapy – competing with or complementary to revascularisation in patients with coronary artery disease?

2017· article· en· W2747655903 on OpenAlexaboutno aff
Javaid Iqbal, R. Jay Widmer, Bernard J. Gersh

Bibliographic record

VenueEuroIntervention · 2017
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineConventional PCICoronary artery diseaseInternal medicineCardiologyConcomitantMedical therapyAnginaAcute coronary syndromeArteryUnstable anginaCanadian Cardiovascular SocietyMyocardial infarctionIntensive care medicine

Abstract

fetched live from OpenAlex

The role of coronary revascularisation with PCI and CABG in patients with stable and unstable coronary artery disease (CAD) is well established and there is a general consensus among guidelines as regards the indications for coronary revascularisation. Although revascularisation has undoubtedly revolutionised the treatment of CAD, it is vital to understand the recent advances and importance of the concomitant use of evidence-based optimal medical therapy (OMT). In contemporary practice, OMT should include an antiplatelet agent (or dual antiplatelet therapy when indicated) and a lipid-lowering drug for all patients, and a beta-blocker and an ACE inhibitor (or angiotensin receptor blocker) for the vast majority of patients, along with addressing cardiac risk factors and lifestyle management. OMT is the recommended initial choice for patients with stable angina pectoris, and the indication for revascularisation would be persistence of symptoms despite OMT and/or improvement of prognosis. For patients with acute coronary syndromes or those who underwent coronary revascularisation with either PCI or CABG, long-term use of OMT improves clinical outcomes and prognosis.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.025
GPT teacher head0.295
Teacher spread0.271 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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