A Review of the Evidence for Treatment of Myocardial Infarction With Nonobstructive Coronary Arteries
Bibliographic record
Abstract
Myocardial infarction (MI) with non-obstructive coronary arteries (MINOCA) is reported in 6% of patients with acute MI referred for catheterization. Because of the complex etiology and a limited amount of evidence, the treatment of MINOCA remains elusive. The etiology of MINOCA manifests from several causes including plaque disruption or erosion, epicardial coronary artery vasospasm, and coronary microvascular dysfunction. In addition, spontaneous coronary artery dissection, takotsubo, and myocarditis have been identified as contributing to the diagnosis of MINOCA. Patients with MINOCA are frequently young, non-white females with fewer traditional risk factors compared with those with an MI caused by obstructive coronary disease. Moreover, women who suffered an MI are 5 times more likely to be diagnosed with MINOCA with a trend for worse outcomes compared with men. The increased recognition/diagnosis of MINOCA has highlighted a gap in our understanding of the treatment of MINOCA. This review identified that there is a paucity of evidence on treatment strategies for patients clinically diagnosed with MINOCA, but more importantly that MINOCA should be viewed as a "syndrome" with many different pathologic causes. This suggests that a standard protocol may not be useful for patients with MINOCA. Given the ongoing debate over the complexity of MINOCA, the main focus in the management of MINOCA should be to identify the underlying mechanism for targeted therapies that may optimize outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".