Computer‐assisted quantification of myocardial reperfusion after primary percutaneous coronary intervention predicts functional and contrast‐enhanced cardiovascular magnetic resonance outcomes in patients with ST‐segment elevation myocardial infarction
Bibliographic record
Abstract
OBJECTIVE: We investigated whether the Quantitative Blush Evaluator (QuBE) value predicts functional and contrast-enhanced cardiovascular magnetic resonance (CMR) outcomes at 4-6 months after primary percutaneous coronary intervention (PCI) inpatients with ST-segment elevation myocardial infarction (STEMI). BACKGROUND: QuBEis a computer-assisted open source program to quantify myocardial reperfusion.Although a higher QuBE value is associated with improved myocardial reperfusion measures and lower 1-year mortality, the association with intermediate functional parameters after STEMI has not yet been investigated. METHODS: QuBE values were quantified retrospectively on angiograms of patients enrolled in the ancillary CMR study of the proximal embolic protection in acute myocardial infarction and resolution of ST-elevation trial. QuBE en CMR outcomes were independently assessed by reviewers blinded to clinical data. RESULTS: A higher QuBE value was significantly associated with a smaller left ventricular (LV) end-diastolic and end-systolic volume, a higher LV ejection fraction and systolic wall thickening in the infarct area, and a smaller final infarct size and extent of transmural segments (P ≤ 0.008). In a multivariable model, including age, gender, infarct location, time to treatment, history of myocardial infarction, and postprocedural thrombolysis in myocardial infarction flow grade,only the QuBE value and infarct location remained as independent predictors of LV ejection fraction (P 5 0.018 for QuBE value). CONCLUSION: Higher QuBE values are independently associated with improved functional and contrast-enhanced CMR outcomes including LV ejection fraction at 4-6 months after primary PCI and may therefore aid in identifying high-risk patients who benefit most from adjunctive therapies sustaining myocardial function after PCI.
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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 | 0.005 |
| 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 | 0.000 | 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".