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Record W4307698907 · doi:10.1016/j.jacc.2022.08.795

Coronary Artery Bypass Surgery Without Saphenous Vein Grafting

2022· review· en· W4307698907 on OpenAlexaff
Alistair Royse, Justin Ren, Colin Royse, David H. Tian, Stephen E. Fremes, Mario Gaudino, Umberto Benedetto, Y. Joseph Woo, Andrew B. Goldstone, Piroze Davierwala, Michael A. Borger, Michael P. Vallely, Christopher M. Reid, Rodolfo V. Rocha, David Glineur, Juan B. Grau, Richard E. Shaw, Hugh S. Paterson, Doa El‐Ansary, Stuart Boggett, Nilesh Srivastav, Zulfayandi Pawanis, David Canty, Rinaldo Bellomo

Bibliographic record

VenueJournal of the American College of Cardiology · 2022
Typereview
Languageen
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsOttawa Heart InstituteUniversity of OttawaUniversity of Toronto
Fundersnot available
KeywordsMedicineCardiologyInternal medicineSurgeryArteryRevascularizationBypass graftingSaphenous vein graftStenosisCoronary artery bypass surgeryVeinMyocardial infarction

Abstract

fetched live from OpenAlex

Approximately 95% of patients of any age undergoing contemporary, coronary bypass surgery will receive at least 1 saphenous vein graft (SVG). It is recognized that SVG will develop progressive and accelerated atherosclerosis, resulting in a stenosis, and in occlusion that occurs in 50% by 10 years postoperatively. For arterial conduits, there is little evidence of progressive failure as for SVG. Could avoidance of SVG (total arterial revascularization [TAR]) lead to a different late (>5 year) survival? A literature review of 23 studies (N = 100,314 matched patients) at a mean 8.8 years postoperative found reduced all-cause mortality for TAR (HR: 0.77; 95% CI: 0.71-0.84; P < 0.001). An expanded analysis with a new unpublished data set (N = 63,288 matched patients) was combined with the literature review (N = 127,565). It found reduced all-cause mortality for TAR (HR: 0.78; 95% CI: 0.72-0.85; P < 0.001). Additional Bayesian analysis found a very high probability of a TAR-associated reduction all-cause mortality.

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)0.002
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 score0.938
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0080.006
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.035
GPT teacher head0.306
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.

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

Citations20
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American College of CardiologySame topicCardiac and Coronary Surgery TechniquesFrench-language works237,207