Role of Cardiac Computed Tomography in the Evaluation of Coronary Artery Stenosis in Patients With Ascending Aorta Aneurysm Detected at Transthoracic Echocardiography
Bibliographic record
Abstract
OBJECTIVE: The aim of our study was to evaluate the diagnostic performance of cardiac computed tomography (CCT) in the evaluation of coronary artery stenosis in patients with ascending aorta aneurysm detected at transthoracic echocardiography. METHODS: We conducted a retrospective analysis of patients with an aneurysm 45 mm or greater at transthoracic echocardiography who underwent CCT from 2012 to 2014 in our hospital. We calculated the sensitivity, specificity, and positive and negative predictive values of CCT for the assessment of coronary artery stenosis (<50% or ≥50% stenosis) in patients who underwent conventional coronary angiography. RESULTS: We included 104 patients (73 men, aged 64 [SD, 10.8] years) in our study. Obstructive coronary artery disease was found in 22.1% of patients. Sensitivity, specificity, and positive and negative predictive values of CCT for detecting significant stenoses were 100%, 98%, and 82% and 100% on a segment-by-segment analysis and 100%, 83%, and 65% and 100% on a per-patient analysis, respectively. CONCLUSIONS: Cardiac computed tomography provides a comprehensive evaluation of ascending aorta aneurysms and coronary artery tree.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".