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Record W2890915559 · doi:10.1161/jaha.118.009934

Off‐Pump Coronary Artery Bypass Grafting: 30 Years of Debate

2018· article· en· W2890915559 on OpenAlexaff

Bibliographic record

VenueJournal of the American Heart Association · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsUniversity of OttawaOttawa Heart InstituteSunnybrook Health Science Centre
FundersMedical Research CouncilNational Institute for Health and Care ResearchAstraZenecaBritish Heart FoundationGlaxoSmithKlineAmgen
KeywordsCoronary artery diseaseArteryCoronary artery bypass surgeryMyocardial infarctionPercutaneous coronary interventionBypass grafting

Abstract

fetched live from OpenAlex

Off‐pump coronary artery bypass surgery (OPCAB) has been performed for >30 years. The promotion of OPCAB was based on its potential benefits over some of the limitations of traditional on‐pump coronary artery bypass surgery (ONCAB) by avoiding the trauma of cardiopulmonary bypass (CPB) and by minimizing aortic manipulation. As such, reductions in early mortality and perioperative neurological events, renal failure, blood product transfusions, and hospital length of stay were expected according to the OPCAB proponents. In contrast, critics of OPCAB remain concerned about incomplete and/or poorer quality coronary revascularization with a potential increase in the need for repeat revascularization and late mortality. Despite 3 decades of debate, 115 randomized trials, and >60 meta‐analyses comparing on‐ and off‐pump coronary artery bypass grafting (CABG), controversy on both the role of and indications for OPCAB remains vigorous. In this review, we provide a comprehensive update of the evidence for the differences in the biological effects of off‐ and on‐pump surgery and the comparison of the clinical and angiographic results of the 2 techniques. Furthermore, we critically address the relevant technical aspects of OPCAB, the importance of surgeon experience, and the difference in the costs for the 2 procedures.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.011
GPT teacher head0.271
Teacher spread0.260 · 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

Citations119
Published2018
Admission routes1
Has abstractyes

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