Perfusion Cardiac Magnetic Resonance Imaging as a Rule-Out Test for Cardiac Allograft Vasculopathy
Bibliographic record
Abstract
Cardiac allograft vasculopathy (CAV) is a leading cause of mortality after heart transplantation. Noninvasive imaging techniques used in CAV evaluation have important limitations. In a cross-sectional study, we investigated perfusion cardiac magnetic resonance (CMR) imaging to determine an optimal myocardial perfusion reserve index (MPR) cutoff for detecting CAV using receiver operating characteristic curve analysis. We evaluated CMR performance using sensitivity, specificity and likelihood ratio analysis. We included 29 patients (mean 5 ± 4 years after transplant) scheduled for coronary angiography with intravascular ultrasound (IVUS) who completed CMR. CAV was defined as maximal intimal thickness (MIT) >0.5 mm by IVUS of the left anterior descending artery. CAV was evident in 19 patients (70%) on IVUS (mean MIT 0.82 ± 0.42 mm). MPR was significantly lower in patients with MIT ≥0.50 mm (1.35 ± 0.23 vs. 1.71 ± 0.45, p = 0.013). There was moderate inverse correlation between MPR and MIT (r = -0.36, p = 0.075). The optimal MPR cutoff ≤1.68 for predicting CAV showed sensitivity of 100%, specificity of 63%, a negative predictive value of 100%, a positive predictive value of 86% and a positive likelihood ratio of 2.7. An MPR ≤1.68 has high negative predictive value, suggesting its potential as a test to rule out CAV.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".