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Record W3091884451 · doi:10.1097/hco.0000000000000783

Secondary prevention after CABG: do new agents change the paradigm?

2020· review· en· W3091884451 on OpenAlexafffund
Amélie Paquin, Paul Poirier, Jonathan Beaudoin, Marie‐Ève Piché

Bibliographic record

VenueCurrent Opinion in Cardiology · 2020
Typereview
Languageen
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersCanadian Institutes of Health Research
KeywordsMedicineCoronary artery diseaseStroke (engine)CardiologyInternal medicineArteryDiseaseIntensive care medicineSecondary preventionAdverse effect

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Coronary artery bypass graft (CABG) surgery remains the gold-standard treatment for multivessel and left main coronary artery disease. Despite significant improvement in cardiovascular outcomes, patients undergoing CABG remain at risk for recurrent adverse ischemic events and other cardiovascular outcomes (coronary revascularisation, stroke, cardiac death, etc.). The purpose of this review is to summarize the most recent evidence in pharmacological preventive therapies addressing the residual cardiovascular risk in patients who have undergone CABG. RECENT FINDINGS: Novel cardiovascular pharmacological preventive strategies targeting inflammatory, metabolic and prothrombotic (antiplatelet and anticoagulation) pathways have been recently assessed, with promising results for secondary prevention after CABG. SUMMARY: Secondary prevention is an essential part of postoperative care after CABG. Novel lipid-lowering and glucose-controlling agents suggest a strong and consistent benefit on native coronary artery disease and overall cardiovascular outcomes. The role and the choice of enhanced antiplatelet/anticoagulation/lipid/glucose-modulating therapies following CABG should be better defined and deserves further investigation. Additional studies are required to identify new therapeutic target addressing the specific multifactorial nature of the graft CV disease and identifying the best preventive strategies for long-term graft patency.

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 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 score0.952
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
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 head0.180
GPT teacher head0.422
Teacher spread0.241 · 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

Citations16
Published2020
Admission routes2
Has abstractyes

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