Quantitative stress perfusion cardiovascular magnetic resonance: clinical implications for patients with suspected myocardial ischemia
Bibliographic record
Abstract
INTRODUCTION: Recent technical advances in stress perfusion cardiovascular magnetic resonance (CMR) imaging allow for myocardial blood flow (MBF) quantification (quantitative perfusion CMR [QP CMR]). However, clinical utility of QP CMR, as compared with conventional grayscale qualitative assessment (QA), is unknown. OBJECTIVES: The study aimed to compare the clinical conclusions on ischemia detection derived from QA of conventional grayscale stress perfusion CMR images and QP CMR in a real‑world population of patients with suspected myocardial ischemia. PATIENTS AND METHODS: This study retrospectively analyzed 101 patients with suspected myocardial ischemia referred for adenosine stress perfusion CMR imaging. QA of grayscale first‑pass perfusion CMR was performed by level 3 CMR experts. In QP assessment, stress and rest MBF (ml/g/min) were calculated for automatically determined myocardial segments. Each patient and coronary territory were classified by both QA and QP mapping, in a blind manner, as either ischemic or nonischemic. RESULTS: QP assessment classified more coronary territories as ischemic than QA (46% vs 17%; P <0.001). In the per‑patient analysis, QP analysis identified myocardial ischemia in 64 patients (63%), and QA in 40 (40%; P <0.001). Ischemia was diagnosed by QA but not by QP analysis in 7% of the patients (QA+/QP-). In 31% of the patients, QP assessment established a new diagnosis of myocardial ischemia (QA-/QP+). CONCLUSION: QP CMR detects more ischemic coronary territories than QA and holds promise for identifying cases of myocardial ischemia that may be overlooked by QA alone in a real‑world patient population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".