Incidental detection of myocardial clefts in a patient with acute inferior ST-segment elevation myocardial infarction: a very unusual and potentially ominous association—a case-report
Bibliographic record
Abstract
BACKGROUND: The crescent availability of high-resolution cardiac imaging allows detection of myocardial structural variations. Differentiate these entities from others with different clinical significance can be challenging. Clinicians should be familiar with myocardial clefts to avoid erroneous diagnosis. CASE SUMMARY: A 63-year-old smoker man alerted the emergency medical system for sudden chest pain. The electrocardiogram showed Pardee wave in inferior leads. Coronary angiography evidenced a 100% occlusion of right coronary artery that was treated by angioplasty and drug-eluting stent implantation with optimal angiographic result. At ventriculography, two fissure-like protrusion were observed in the inferior wall. Urgent transthoracic echocardiogram (TTE) demonstrated two deep fissures on the mid-inferior wall, contained by a thin sub-epicardial layer, with sub-total obliteration during systole. A diagnosis of myocardial clefts was suspected and after Heart Team discussion, a conservative strategy was proposed. Early cardiac magnetic resonance (CMR) confirmed two myocardial crypts on the mid-inferior wall. Stability of myocardial fissures and absence of left ventricular remodelling was confirmed by TTE, in a 2 years of follow-up period. DISCUSSION: Myocardial cleft should always be considered in the differential diagnosis of myocardial wall defects. In a patient presenting with an acute myocardial infarction, the main differential diagnosis is pseudoaneurysm. In this setting modified TTE views and meticulous analysis of CMR sequences are recommended to confirm the diagnosis and estimate the risk of myocardial rupture.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".