Abstract 2808: Combination of Adenosine Stress Perfusion and Late Enhancement Cardiac Magnetic Resonance Imaging in Patients with Suspected Coronary Artery Disease, Percutaneous Coronary Intervention and Coronary Bypass Graft -A Multi-Center Study
Bibliographic record
Abstract
The combination of stress perfusion and late Gadolinium enhancement (LGE) cardiac magnetic resonance imaging (CMR) has been established for diagnosis of myocardial ischemia. However, little is known about this helpful clinical examination tool in patients who were treated by percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG). Aim of our study was to compare the diagnostic accuracy of stress perfusion and LGE in patients with suspected coronary artery disease (CAD), with PCI and with CABG in a multi-center trial. 477 patients with suspected CAD, 209 with PCI and 110 with CABG were included to the study and underwent adenosine stress perfusion and LGE 10 min. after a second bolus of contrast agent. CMR images were assessed visually using the 16-segments model. Myocardial ischemia was defined as resgional hypoenhancement in stress perfusion with absent LGE. All patients underwent coronary angiography. A significant stenosis was defined by QCA in case of ≥70% of coronary artery or bypass graft narrowing in vessels ≥2 mm diameter. A relevant vessel stenosis or occlusion was present in 173 (36%) patients with susptectd CAD, 69 (29%) PCI and 71 (65%) CABG patients. PCI was performed 314±231 and CABG 423±275 days before CMR examination. Sensitivity, specificity and overall accuracy per patient are given in table 1 CMR is feasible and suitable for detecting relevant vessel stenosis in patients who previously were treated by PCI or CABG. Diagnostic accuracy is reduced in patients with CABG. This could be due to different flow and perfusion kinetic. Furthermore, presented evaluation method may be inadequate, since collaterals and different perfusion territories are not taken into consideration. CMR yields similar diagnostic accuracy in patients with suspected CAD and those who previously were treated by PCI.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".