Coronary computed tomography–angiography quantitative plaque analysis improves detection of early cardiac allograft vasculopathy: A pilot study
Bibliographic record
Abstract
Cardiac allograft vasculopathy (CAV) is an increasingly important complication after cardiac transplant. We assessed the additive diagnostic benefit of quantitative plaque analysis in patients undergoing coronary computed tomography-angiography (CCTA). Consecutive patients undergoing CCTA for CAV surveillance were identified. Scans were visually interpreted for coronary stenosis. Semiautomated software was used to quantify noncalcified plaque (NCP), as well as its components. Optimal diagnostic cut-offs for CAV, with coronary angiography as gold standard, were defined using receiver operating characteristic curves. In total, 36 scans were identified in 17 patients. CAV was present in 17 (46.0%) reference coronary angiograms, at a median of 1.9 years before CCTA. Median NCP (147 vs 58, P < .001), low-density NCP (median 4.5 vs 0.9, P = .003), fibrous plaque (median 76.1 vs 31.1, P = .003), and fibrofatty plaque (median 63.6 vs 27.6, P < .001) volumes were higher in patients with CAV, whereas calcified plaque was not (median 0.0 vs 0.0, P = .510). Visual assessment of CCTA alone was 70.6% sensitive and 100% specific for CAV. The addition of total NCP volume increased sensitivity to 82.4% while maintaining 100% specificity. NCP volume is significantly higher in patients with CAV. The addition of quantitative analysis to visual interpretation improves the sensitivity for detecting CAV without reducing specificity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".