Computed tomography angiography and perfusion to assess coronary artery stenosis causing perfusion defects by single photon emission computed tomography: the CORE320 study
Bibliographic record
Abstract
AIMS: To evaluate the diagnostic power of integrating the results of computed tomography angiography (CTA) and CT myocardial perfusion (CTP) to identify coronary artery disease (CAD) defined as a flow limiting coronary artery stenosis causing a perfusion defect by single photon emission computed tomography (SPECT). METHODS AND RESULTS: We conducted a multicentre study to evaluate the accuracy of integrated CTA-CTP for the identification of patients with flow-limiting CAD defined by ≥50% stenosis by invasive coronary angiography (ICA) with a corresponding perfusion deficit on stress single photon emission computed tomography (SPECT/MPI). Sixteen centres enroled 381 patients who underwent combined CTA-CTP and SPECT/MPI prior to conventional coronary angiography. All four image modalities were analysed in blinded independent core laboratories. The prevalence of obstructive CAD defined by combined ICA-SPECT/MPI and ICA alone was 38 and 59%, respectively. The patient-based diagnostic accuracy defined by the area under the receiver operating characteristic curve (AUC) of integrated CTA-CTP for detecting or excluding flow-limiting CAD was 0.87 [95% confidence interval (CI): 0.84-0.91]. In patients without prior myocardial infarction, the AUC was 0.90 (95% CI: 0.87-0.94) and in patients without prior CAD the AUC for combined CTA-CTP was 0.93 (95% CI: 0.89-0.97). For the combination of a CTA stenosis ≥50% stenosis and a CTP perfusion deficit, the sensitivity, specificity, positive predictive, and negative predicative values (95% CI) were 80% (72-86), 74% (68-80), 65% (58-72), and 86% (80-90), respectively. For flow-limiting disease defined by ICA-SPECT/MPI, the accuracy of CTA was significantly increased by the addition of CTP at both the patient and vessel levels. CONCLUSIONS: The combination of CTA and perfusion correctly identifies patients with flow limiting CAD defined as ≥50 stenosis by ICA causing a perfusion defect by SPECT/MPI.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".