Diagnostic Performance of Treadmill Exercise Cardiac Magnetic Resonance: The Prospective, Multicenter Exercise CMR's Accuracy for Cardiovascular Stress Testing (EXACT) Trial
Bibliographic record
Abstract
BACKGROUND: Stress cardiac magnetic resonance (CMR) has typically involved pharmacologic agents. Treadmill CMR has shown utility in single-center studies but has not undergone multicenter evaluation. METHODS AND RESULTS: Patients referred for treadmill stress nuclear imaging (SPECT) were prospectively enrolled across 4 centers. After rest (99m)Tc SPECT, patients underwent resting cine CMR. In-room stress was then performed using an MR-compatible treadmill with continuous 12-lead electrocardiogram monitoring. At peak stress, (99m)Tc was injected, and patients rapidly returned to the MR scanner isocenter for real-time, free-breathing stress cine and perfusion imaging. After recovery, cine and rest perfusion followed by late gadolinium enhancement acquisitions concluded CMR imaging. Stress SPECT was then acquired in adjacent nuclear laboratories. A subset of patients not referred for invasive coronary angiography within 2 weeks of stress underwent coronary computed tomography angiography. Angiographic data available in 94 patients showed sensitivity of 79%, specificity of 99% for exercise CMR with positive predictive value of 92% and negative predictive value of 96%. Agreement between treadmill stress CMR and angiography was strong (κ=0.82), and moderate between SPECT and angiography (κ=0.46) and CMR versus SPECT (κ=0.48). CONCLUSIONS: The multicenter EXACT trial indicates excellent diagnostic value of treadmill stress CMR in typical patients referred for exercise SPECT.
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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 | 0.008 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".