Whole-Heart Coronary Magnetic Resonance Angiography at 1.5 Tesla
Bibliographic record
Abstract
OBJECTIVES: To evaluate the impact of the blood-pool contrast agent gadofosveset trisodium on diagnostic accuracy of whole-heart coronary magnetic resonance angiography (CMRA) at 1.5 Tesla. MATERIALS AND METHODS: Thirty consecutive patients with suspected coronary artery disease underwent free-breathing whole-heart CMRA at 1.5 Tesla. CMRA was performed with a T2-prepared steady-state free precession sequence (unenhanced CMRA) and an inversion-recovery-prepared steady-state free precession sequence after administration of gadofosveset trisodium (contrast-enhanced CMRA). Two readers independently performed a per-segment evaluation of CMRA (8 proximal and mid coronary segments) for detection of significant stenosis (≥50%) using invasive coronary angiography as reference. Disagreement was settled by consensus reading and interobserver variability was assessed using an unweighted kappa statistic. RESULTS: Whole-heart CMRA was successfully performed in 27 patients. The percentage of assessable segments was significantly lower on unenhanced CMRA compared with contrast-enhanced CMRA (Reader 1: 79% [170/216] vs. 89% [192/216], respectively; Reader 2: 73% [157/216] vs. 87% [188/216], respectively; P < 0.001). Intention-to-diagnose analysis of the consensus reading yielded sensitivity, specificity, and diagnostic accuracy of unenhanced versus contrast-enhanced CMRA as follows: 73.1% versus 73.1% (P = 1.0), 68.3% versus 80.2% (P = 0.002), and 68.9% versus 79.3% (P = 0.004), respectively. The kappa value for interobserver agreement was 0.61 (95% confidence interval = 0.50-0.72) for unenhanced CMRA and 0.72 (95% confidence interval = 0.62-0.82) for contrast-enhanced CMRA. CONCLUSIONS: The blood-pool contrast agent gadofosveset trisodium increased the number of assessable coronary segments on whole-heart CMRA in comparison to unenhanced whole-heart CMRA. The impact of gadofosveset trisodium on diagnostic accuracy, however, was only minor.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".