Myocardial infarction with nonobstructive coronary artery disease (MINOCA): a review of pathophysiology and management
Bibliographic record
Abstract
PURPOSE OF REVIEW: Myocardial infarction with nonobstructive coronary artery disease (MINOCA) (≥ 50% stenosis) accounts for 5-8% of acute coronary syndrome (ACS) presentations. The demographic characteristics, risk factors, and management of patients with MINOCA differ from those with atherosclerotic ACS. The objective of this review is to provide a contemporary understanding of the epidemiology, pathophysiology, clinical presentation, and management of MINOCA. RECENT FINDINGS: MINOCA is increasingly being recognized as an important and distinct cause of myocardial infarction among patients presenting with ACS. The predominant pathophysiologic mechanisms of MINOCA include both coronary (epicardial vasospasm, coronary microvascular disorder, spontaneous coronary artery dissection, coronary thrombus/embolism) and noncoronary (Takotsubo cardiomyopathy, myocarditis) pathologies. Coronary imaging with intravascular ultrasound and optical coherent tomography, coronary physiology testing, and cardiac magnetic resonance imaging offers important investigative modalities to facilitate diagnosis for appropriate management of MINOCA patients. SUMMARY: MINOCA is an important cause of ACS observed in certain patients with unique challenges for diagnosis and management. A high index of suspicion and a comprehensive diagnostic evaluation are critical for early recognition and successful management.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".