Coronary morphological features in women with non-ST-segment elevation MINOCA and MI-CAD as assessed by optical coherence tomography
Bibliographic record
Abstract
Aims: We aimed to use optical coherence tomography (OCT) to identify differences in atherosclerotic culprit lesion morphology in women with myocardial infarction (MI) with non-obstructive coronary arteries (MINOCA) compared with MI with obstructive coronary artery disease (MI-CAD). Methods and results: Women with an OCT-determined atherosclerotic aetiology of non-ST segment elevation (NSTE)-MINOCA (angiographic diameter stenosis <50%) who were enrolled in the multicentre Women's Heart Attack Research Program (HARP) study were compared with a consecutive series of women with NSTE-MI-CAD who underwent OCT prior to coronary intervention at a single institution. Atherosclerotic pathologies identified by OCT included plaque rupture, plaque erosion, intraplaque haemorrhage (IPH, a region of low signal intensity with minimum attenuation adjacent to a lipidic plaque without fibrous cap disruption), layered plaque (superficial layer with clear demarcation from the underlying plaque indicating early thrombus healing), or eruptive calcified nodule.We analysed 58 women with NSTE-MINOCA and 52 women with NSTE-MI-CAD. Optical coherence tomography features of underlying vulnerable plaque (thin-cap fibroatheroma) were less common in MINOCA (3 vs. 35%) than in MI-CAD. Intraplaque haemorrhage (47 vs. 2%) and layered plaque (31 vs. 12%) were more common in MINOCA than MI-CAD, whereas plaque rupture (14 vs. 67%), plaque erosion (8 vs. 14%), and calcified nodule (0 vs. 6%) were less common in MINOCA. The angle of ruptured cavity was smaller and thrombus burden was lower in MINOCA. Conclusion: The prevalence of atherothrombotic culprit lesion subtype varied substantially between MINOCA and MI-CAD. A majority of culprit lesions in MINOCA had the appearance of IPH or layered plaque. Clinical Trial Registration Information: : https://clinicaltrials.gov/ct2/show/NCT02905357.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".