A randomized, multicenter, multivendor study of myocardial perfusion imaging with regadenoson CT perfusion vs single photon emission CT
Bibliographic record
Abstract
BACKGROUND: Myocardial CT perfusion (CTP) is a promising tool for the detection of myocardial ischemia. We hypothesize that regadenoson CTP is noninferior to regadenoson single photon emission CT (SPECT) for detecting or excluding myocardial ischemia. METHODS: Patients (men ≥ 45 years; women ≥ 50 years) with known or suspected coronary artery disease (n = 124) were randomized to 1 of 2 diagnostic sequences: rest and regadenoson SPECT on day 1, then regadenoson CTP and rest CTP (and coronary CT angiography [CTA]) (CTA; same acquisition) on day 2 or regadenoson CTP and rest CTP (and CTA) on Day 1, then rest and regadenoson SPECT on day 2. Scanning platforms included 64-, 128-, 256-, and 320-slice systems. The primary analysis examined the agreement rate between CTP and SPECT for detecting or excluding reversible ischemia in ≥ 2 myocardial segments as assessed by independent, blinded readers. RESULTS: Complete and interpretable CTP and SPECT scans were obtained for 110 patients. Regadenoson CTP was noninferior to SPECT for detecting or excluding reversible ischemia with an agreement rate of 0.87 (95% confidence interval [CI], 0.77-0.97) and sensitivity and specificity of 0.90 (95% CI, 0.71-1.00) and 0.84 (95% CI, 0.77-0.91), respectively. The agreement rate for detecting or excluding ≥ 1 fixed defects by regadenoson CTP and SPECT was 0.86 (95% CI, 0.74-0.98). With SPECT as the reference standard, the diagnostic accuracies for detecting or excluding ischemia by regadenoson CTP and CTA alone were 0.85 (95% CI, 0.78-0.91) and 0.69 (95% CI, 0.60-0.77), respectively. CONCLUSIONS: This study establishes the noninferiority of regadenoson CTP to SPECT for detecting or excluding myocardial ischemia.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| 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".