Angiographic estimates of myocardium at risk during acute myocardial infarction: validation study using cardiac magnetic resonance imaging
Bibliographic record
Abstract
AIMS: Global angiographic scores have been developed to determine the extent of myocardium jeopardized by significant coronary stenosis. We adapted these scores to quantify the anatomic area at risk during acute myocardial infarction. We used contrast-enhanced magnetic resonance (CMR) infarct imaging to measure the portion of myocardium that developed necrosis within the so defined angiographic area at risk. METHODS AND RESULTS: In 83 subjects presenting for primary percutaneous intervention, the myocardium at risk was estimated angiographically using the Myocardial Jeopardy Index (BARI) and a modified version of the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) scores. CMR was performed within a week to measure infarct size, infarct endocardial surface area (infarct-ESA), and infarct transmurality. As infarct transmurality increased, the infarct size closely approximated the myocardium at risk by angiography. In 35 subjects with transmural infarcts, the area at risk by BARI and APPROACH scores matched the infarct size (r = 0.90 and r = 0.92, P < 0.001). Additionally, BARI and APPROACH scores matched the infarct-ESA in all subjects independently of collateral flow and time to reperfusion (r = 0.90 and r = 0.87, P < 0.001). The presence of early reperfusion, collaterals, or both was associated with a progressive decrease in infarct transmurality (P < 0.001 for trend) with no difference in the infarct-ESA. CONCLUSION: The myocardium at risk of infarction can be determined angiographically as validated in subjects with transmural myocardial infarcts. Salvage provided by early reperfusion or collaterals occurs by limiting infarct transmurality, thereby the extent of endocardial infarct involved also allows estimation of the myocardium at risk in patients presenting with STEMI.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.000 | 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".