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Record W2058746149 · doi:10.1093/eurheartj/eht284

Coronary artery bypass grafting: Part 2--optimizing outcomes and future prospects

2013· review· en· W2058746149 on OpenAlexaff
Stuart J. Head, Jochen Börgermann, Ruben L.J. Osnabrugge, Teresa M. Kieser, Volkmar Falk, David P. Taggart, John D. Puskas, J Gummert, A. Pieter Kappetein

Bibliographic record

VenueEuropean Heart Journal · 2013
Typereview
Languageen
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsLibin Cardiovascular Institute of Alberta
Fundersnot available
KeywordsMedicineConventional PCIBypass graftingArteryPercutaneous coronary interventionRandomized controlled trialRevascularizationCardiologyCoronary artery diseaseInternal medicineIntensive care medicineSurgeryMyocardial infarction

Abstract

fetched live from OpenAlex

Since first introduced in the mid-1960s, coronary artery bypass grafting (CABG) has become the standard of care for patients with coronary artery disease. Surprisingly, the fundamental surgical technique itself did not change much over time. Nevertheless, outcomes after CABG have dramatically improved over the first 50 years. Randomized trials comparing percutaneous coronary intervention (PCI) to CABG have shown converging outcomes for select patient populations, providing more evidence for wider use of PCI. It is increasingly important to focus on the optimization of the short- and long-term outcomes of CABG and to reduce the level of invasiveness of this procedure. This review provides an overview on how new techniques and widespread consideration of evolving strategies have the potential to optimize outcomes after CABG. Such developments include off-pump CABG, clampless/anaortic CABG, minimally invasive CABG with or without extending to hybrid procedures, arterial revascularization, endoscopic vein harvesting, intraprocedural epiaortic scanning, graft flow assessment, and improved secondary prevention measures. In addition, this review represents a framework for future studies by summarizing the areas that need more rigorous clinical (randomized) evaluation.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.908
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.057
GPT teacher head0.321
Teacher spread0.264 · 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 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

Citations130
Published2013
Admission routes1
Has abstractyes

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