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Record W1997668423 · doi:10.1093/qjmed/hcs195

Huge aortic pseudoaneurysm arising from the coronary anastomosis after aortic root replacement

2012· article· en· W1997668423 on OpenAlexaff
Jehangir Din, Anthony Della Siega, Shawna Silver, Daniel Wong, Simon D. Robinson

Bibliographic record

VenueQJM · 2012
Typearticle
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsRoyal Columbian HospitalRoyal Jubilee HospitalVictoria Heart Institute Foundation
Fundersnot available
KeywordsAortic rootPseudoaneurysmCardiologyInternal medicineMedicineAnastomosisAortaSurgeryComplication

Abstract

fetched live from OpenAlex

An 87-year-old man was admitted with chest pain. His troponin was elevated and his electrocardiogram showed anterior ST depression. The patient had undergone aortic root replacement 13 years earlier using a mechanical valved conduit with re-implantation of the coronary arteries. Although lost to follow-up, he continued on warfarin. He initially received treatment for a suspected acute coronary syndrome. However, a subsequent echocardiogram suggested a circumferential haematoma around the aorta. Contrast computed tomography (CT) demonstrated a large aortic pseudoaneurysm arising from the left coronary anastamosis (Figure 1A–C). There was complete dehiscence of the coronary anastomosis from the aorta with reduced contrast opacification into the left main coronary artery suggesting impaired blood flow in this territory. As the risk of surgical repair was considered prohibitive, the patient was managed conservatively with cessation of anti-coagulation. The patient’s chest pain resolved and a repeat CT 6 days later demonstrated improved contrast opacification and coronary blood flow in the left coronary artery (Figure 1D). Echocardiography at 6 months demonstrated no expansion of the aortic pseudoaneurysm, and the patient remains off anti-coagulation and free of cardiac complications at 12 months.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.023
GPT teacher head0.267
Teacher spread0.244 · 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 designObservational
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

Citations0
Published2012
Admission routes1
Has abstractyes

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