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Arterial Grafts for Coronary Bypass

2019· review· en· W2978057393 on OpenAlexaff
Mario Gaudino, Faisal G. Bakaeen, Umberto Benedetto, Antonino Di Franco, Stephen E. Fremes, David Glineur, Leonard N. Girardi, Juan B. Grau, John D. Puskas, Marc Ruel, Derrick Y. Tam, David P. Taggart, Charalambos Antoniades, Thomas A. Schwann, James Tatoulis, Robert F. Tranbaugh

Bibliographic record

VenueCirculation · 2019
Typereview
Languageen
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsUniversity of OttawaSunnybrook Health Science Centre
FundersBritish Heart Foundation
KeywordsMedicineCardiologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

Observational and randomized evidence shows that arterial grafts have better patency rates than saphenous vein grafts (SVGs) in coronary artery bypass grafting. Observational studies suggest that the use of multiple arterial grafts is associated with longer postoperative survival, but this must be interpreted in the context of treatment allocation bias and hidden confounders intrinsic to the study designs. Recently, a pooled analysis of 6 randomized trials comparing the radial artery with the SVG as the second conduit and the largest randomized trial comparing the use of single and bilateral internal thoracic arteries have provided apparently divergent results about a clinical benefit with the use of >1 arterial conduit. However, both analyses have methodological limitations that may have influenced their results. At present, it is unclear whether the well-documented increased patency rate of arterial grafts translates into clinical benefits in the majority of patients undergoing coronary artery bypass grafting. A large randomized trial testing the arterial grafts hypothesis (ROMA [Randomized Comparison of the Clinical Outcome of Single Versus Multiple Arterial Grafts]) is underway and will report the results in a few years.

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 score0.987
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.072
GPT teacher head0.350
Teacher spread0.277 · 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

Citations81
Published2019
Admission routes1
Has abstractyes

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