Prediction of Coronary Revascularization in Stable Angina
Bibliographic record
Abstract
This study was designed to compare head-to-head fractional flow reserve (FFR) derived from coronary computed tomography angiography (CTA) (FFRCT) and cardiac magnetic resonance (CMR) stress perfusion imaging for prediction of standard-of-care–guided coronary revascularization in patients with stable chest pain and obstructive coronary artery disease by coronary CTA. FFRCT is a novel modality for noninvasive functional testing. The clinical utility of FFRCT compared to CMR stress perfusion imaging in symptomatic patients with coronary artery disease is unknown. Prospective study of patients (n = 110) with stable angina pectoris and 1 or more coronary stenosis ≥50% by coronary CTA. All patients underwent invasive coronary angiography. Revascularization was FFR-guided in stenoses ranging from 30% to 90%. FFRCT ≤0.80 in 1 or more coronary artery or a reversible perfusion defect (≥2 segments) by CMR categorized patients with ischemia. FFRCT and CMR were analyzed by core laboratories blinded for patient management. A total of 38 patients (35%) underwent revascularization. Per-patient diagnostic performance for identifying standard-of-care–guided revascularization, (95% confidence interval) yielded a sensitivity of 97% (86% to 100%) for FFRCT versus 47% (31% to 64%) for CMR, p < 0.001; corresponding specificity was 42% (30% to 54%) versus 88% (78% to 94%), p < 0.001; negative predictive value of 97% (91% to 100%) versus 76% (67% to 85%), p < 0.05; positive predictive value of 47% (36% to 58%) versus 67% (49% to 84%), p < 0.05; and accuracy of 61% (51% to 70%) versus 74% (64% to 82%), p > 0.05, respectively. In patients with stable chest pain referred to invasive coronary angiography based on coronary CTA, FFRCT and CMR yielded similar overall diagnostic accuracy. Sensitivity for prediction of revascularization was highest for FFRCT, whereas specificity was highest for CMR.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| 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".