MRI and CT in the diagnosis of coronary artery disease: indications and applications
Bibliographic record
Abstract
In recent years, technical advances and improvements in cardiac computed tomography (CT) and cardiac magnetic resonance imaging (MRI) have provoked increasing interest in the potential clinical role of these techniques in the non-invasive work-up of patients with suspected coronary artery disease (CAD) and correct patient selection for these emerging imaging techniques. In the primary detection or exclusion of significant CAD, e.g. in the patient with unspecific thoracic complaints, and also in patients with known CAD or advanced stages of CAD, both CT and MRI yield specific advantages. In this review, the major aspects of non-invasive MR and CT imaging in the diagnosis of CAD will be discussed. The first part describes the clinical value of contrast-enhanced non-invasive CT coronary angiography (CTCA), including the diagnostic accuracy of CTCA for the exclusion or detection of significant CAD with coronary artery stenoses that may require angioplastic intervention, as well as potentially valuable information on the coronary artery vessel wall. In the second section, the potential of CT for the imaging of myocardial viability and perfusion will be highlighted. In the third and final part, the range of applications of cardiac MRI in CAD patients will be outlined.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".