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Record W3107559267 · doi:10.1093/ejcts/ezaa377

How to build a multi-arterial coronary artery bypass programme: a stepwise approach

2020· article· en· W3107559267 on OpenAlexaff
Mario Gaudino, Sigrid Sandner, Giorgia Bonalumi, Jennifer S. Lawton, Stephen E. Fremes

Bibliographic record

VenueEuropean Journal of Cardio-Thoracic Surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsMedicineObservational studyMyocardial revascularizationRevascularizationArteryCardiologyInternal medicineCoronary artery bypass surgerySurgeryMyocardial infarction

Abstract

fetched live from OpenAlex

Observational evidence shows that the use of multiple arterial grafts (MAG) is associated with longer postoperative survival and improved clinical outcomes. The current European Society of Cardiology/European Association for Cardio-Thoracic Surgery Guidelines on myocardial revascularization recommend the use of MAG in appropriate patients. However, a significant volume-to-outcome relationship exists for MAG, and lack of sufficient experience is associated with increased operative risk. A stepwise approach to building experience with MAG allows successful implementation of this technique into routine coronary surgery practice.

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)0.005
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.833
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
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 head0.053
GPT teacher head0.275
Teacher spread0.222 · 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
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

Citations22
Published2020
Admission routes1
Has abstractyes

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