Diagnosing coronary artery disease after a positive coronary computed tomography angiography: the Dan-NICAD open label, parallel, head to head, randomized controlled diagnostic accuracy trial of cardiovascular magnetic resonance and myocardial perfusion scintigraphy
Bibliographic record
Abstract
Aims: Perfusion scans after coronary computed tomography angiography (CCTA) in patients with suspected coronary artery disease (CAD) may reduce unnecessary invasive coronary angiographies (ICAs). However, the diagnostic accuracy of perfusion scans after primary CCTA is unknown. The aim of this study was to determine the diagnostic accuracy of cardiac magnetic resonance (CMR) and myocardial perfusion scintigraphy (MPS) against ICA with fractional flow reserve (FFR) in patients suspected of CAD by CCTA. Methods and results: Included were consecutive patients (1675) referred to CCTA with symptoms of CAD and low/intermediate risk profile. Patients with suspected CAD based on CCTA were randomized 1:1 to CMR or MPS followed by ICA with FFR. Obstructive CAD was defined as FFR ≤ 0.80 or > 90% diameter stenosis by visual assessment. After initial CCTA, 392 patients (23%) were randomized; 197 to CMR and 195 to MPS. Perfusion scans and ICA were completed in 292 patients (CMR 148, MPS 144). Based on the ICA, 117/292 (40%) patients were classified with CAD. Sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) for CMR were 41%, 95% CI [28-54], 84% [75-91], 62% [45-78], and 68% [58-76], respectively. For the MPS group 36% [24-50], 94% [87-98], 81% [61-93], and 68% [59-76], respectively. Conclusion: Patients with low/intermediate CAD risk and a positive CCTA scan represent a challenge to perfusion techniques indicated by the low sensitivity of both CMR and MPS with FFR as a reference. The mechanisms underlying this discrepancy need further investigation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".