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Record W2800901908 · doi:10.21037/acs.2018.03.14

Management of the difficult left subclavian artery during aortic arch repair

2018· article· en· W2800901908 on OpenAlex
Ali Hage, Olivia Ginty, Adam Power, Luc Dubois, François Dagenais, Jehangir J. Appoo, John Bozinovski, Michael Chu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnals of Cardiothoracic Surgery · 2018
Typearticle
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsUniversity of British ColumbiaLibin Cardiovascular Institute of AlbertaUniversity of CalgaryRoyal Jubilee HospitalLawson Health Research InstituteWestern University
Fundersnot available
KeywordsMedicineAortic archLeft subclavian arteryStroke (engine)Subclavian steal syndromeAneurysmSubclavian arterySurgeryAortaCardiologyRadiology

Abstract

fetched live from OpenAlex

Management of the left subclavian artery (SCA) during aortic arch surgery is associated with several challenges, including preserving distal perfusion, achieving hemostasis and preventing posterior circulation stroke and spinal cord injury. The most common challenge remains its deep position in the chest, often exacerbated by posterior and apical displacement from an arch aneurysm. We discuss several management options consisting of pre-, intra- and post-operative strategies and their respective advantages, disadvantages and clinical outcomes. A clinical algorithm is proposed to help guide decision-making in managing the difficult left SCA during aortic arch repair.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.489

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 head0.057
GPT teacher head0.331
Teacher spread0.273 · 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