Cardiovascular magnetic resonance for diagnosis of coronary artery disease:<i>quo vadis</i>?
Bibliographic record
Abstract
Cardiovascular magnetic resonance imaging (CMR) has emerged as a potential modality for the diagnosis and risk stratification of patients with documented or suspected coronary artery disease. As such, it may be used as an alternative to other accepted noninvasive modalities. In the Clinical Evaluation of Magnetic Resonance Imaging in Coronary Heart Disease (CE-MARC) study, Greenwood et al. enrolled 752 patients with suspected angina pectoris and at least one cardiovascular risk factor, and evaluated the diagnostic accuracy of multiparametric CMR and single photon emission computed tomography (SPECT), and compared them with invasive coronary angiography as the reference standard. The authors reported significantly higher sensitivity and negative predictive values for CMR (86.5 and 90.5%, respectively) compared with SPECT (66.5 and 79.1%, respectively) and recommended that CMR be used more frequently than at present for the investigation of coronary artery disease. This robustly designed landmark trial certainly adds to the already impressive diagnostic data available with CMR in such patients, but being a new technique, it lacks the large outcome data available with SPECT. In summary, the results of this study confirm the promise for CMR, but further work and larger multicenter studies are required before its adoption into routine clinical practice.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.021 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".