Reproducibility of myocardial extracellular volume quantification using dual-energy computed tomography in patients with cardiac amyloidosis
Bibliographic record
Abstract
Background Quantifying myocardial extracellular volume (ECV) using computed tomography (CT) has been shown to be useful in the evaluation of cardiac amyloidosis. However, the reproducibility of CT measurements for myocardial ECV, is not well-established in patients with proven cardiac amyloidosis. Methods This prospective single-center study enrolled cardiac amyloidosis patients to undergo dual-energy CT for myocardial fibrosis assessment. Delayed imaging at 7 and 8 min post-contrast and independent evaluations by two blinded cardiologists were performed for ECV quantification using 16-segment (ECV global ) and septal sampling (ECV septal ). Inter- and intraobserver variability and test-retest reliability were measured using Spearman's rank correlation, Bland-Altman analysis, and intraclass correlation coefficients (ICC). Results Among the 24 participants (median age = 78, 67 % male), CT ECV global and ECV septal showed median values of 53.6 % and 49.1 % at 7 min, and 53.3 % and 50.1 % at 8 min, respectively. Inter- and intraobserver variability and test-retest reliability for CT ECV global (ICC = 0.798, 0.912, and 0.894, respectively) and ECV septal (ICC = 0.791, 0.898, and 0.852, respectively) indicated good reproducibility, with no evidence of systemic bias between observers or scans. Conclusions Dual-energy CT-derived ECV measurements demonstrated good reproducibility in patients with proven cardiac amyloidosis, suggesting potential utility as a repeatable imaging biomarker for this disease.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".