CMR imaging for the evaluation of myocardial stunning after acute myocardial infarction: a meta-analysis of prospective trials
Bibliographic record
Abstract
BACKGROUND: Myocardial stunning is an important sequela of acute coronary syndromes and its determination might affect decisions on defibrillator implantation and assist devices after myocardial infarction (AMI). The aim of the study was to evaluate and compare the sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) of cardiac magnetic resonance imaging (CMR) assessing myocardial stunning after acute myocardial infarction using low-dose dobutamine (LDD), end-diastolic wall thickness, and contrast delayed enhancement (DE). METHODS AND RESULTS: A systematic review of Medline, Embase, and Cochrane for all prospective trials assessing myocardial stunning by CMR following AMI was performed using a standard approach for meta-analysis for diagnostic test and a bivariate analysis. Search results revealed 9384 studies, out of which 17 met criteria. A total of 634 patients (mean age 59 years, 85% male, mean left ventricular ejection fraction: 52%) were included. DE-CMR had a weighted sensitivity of 87% and specificity of 68% to detect myocardial stunning using 50% transmurality as a cut-off, with a PPV and NPV of 83 and 72%, respectively. With an overall diagnostic accuracy of 82%, LDD-CMR had a sensitivity of 67% and a specificity of 81%, with a PPV and NPV of 82 and 63%, respectively. LDD showed an overall accuracy of 74%. CONCLUSION: DE-CMR has a higher sensitivity, whereas LDD-CMR has a higher specificity for the detection of viable stunned myocardium following myocardial infarction. Whether the combination of DE and LDD may improve the prediction of myocardial recovery remains to be determined.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.036 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".