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Record W2955202017 · doi:10.1097/hco.0000000000000662

Revascularization in left ventricular dysfunction

2019· review· en· W2955202017 on OpenAlexaff
Bobby Yanagawa, Jessica Lee, John D. Puskas, Subodh Verma

Bibliographic record

VenueCurrent Opinion in Cardiology · 2019
Typereview
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineCardiologyRevascularizationInternal medicineMyocardial infarction

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The purpose of this article is to provide an overview of revascularization in patients with coronary artery disease (CAD) and left ventricular dysfunction (LVD). RECENT FINDINGS: Patients with significant CAD and LVD are a high-risk patient population. They make up a minority of the cases from the largest, prospective coronary revascularization trials. The Surgical Treatment for Ischemic Heart Failure (STICH) Trial and its substudies are the most important and well cited in this field. The 10-year data from STICH showed that surgical revascularization was associated with lower all-cause mortality compared with medical therapy. Several smaller studies have confirmed that surgical revascularization carries a significant risk of short-term mortality but overall improved long-term outcomes in patients with LVD. Data from multiple observational studies further confirm that coronary artery bypass graft (CABG) is superior to percutaneous coronary revascularization for long-term survival and freedom from repeat revascularization in patients with LVD. We suggest that patients with LVD undergoing CABG should be considered for multiarterial grafting and that some patients may benefit from an off-pump procedure. SUMMARY: Surgical revascularization confers a long-term survival benefit in patients with significant CAD and LVD. Further studies will be needed to precisely determine the ideal candidate for surgical versus percutaneous revascularization.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.952
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
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 head0.124
GPT teacher head0.411
Teacher spread0.287 · 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 designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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