Recognition of acute myocardial infarction caused by spontaneous coronary artery dissection of first septal perforator
Bibliographic record
Abstract
AIMS: Spontaneous coronary artery dissection (SCAD) diagnosis is challenging as angiographic findings are often subtle and differ from coronary atherosclerosis. Herein, we describe characteristics of patients with acute myocardial infarction (MI) caused by first septal perforator (S1) SCAD. METHODS AND RESULTS: Patients were gathered from SCAD registries at Minneapolis Heart Institute and Vancouver General Hospital. First septal perforator SCAD prevalence was 11 of 1490 (0.7%). Among 11 patients, age range was 38-64 years, 9 (82%) were female. Each presented with acute chest pain, troponin elevation, and non-ST-elevation MI diagnosis. Initial electrocardiogram demonstrated ischaemia in 5 (45%); septal wall motion abnormality was present in 4 (36%). Angiographic type 2 SCAD was present in 7 (64%) patients with S1 TIMI 3 flow in 7 (64%) and TIMI 0 flow in 2 (18%). Initial angiographic interpretation failed to recognize S1-SCAD in 6 (55%) patients (no culprit, n = 5, septal embolism, n = 1). First septal perforator SCAD diagnosis was established by review of initial coronary angiogram consequent to cardiovascular magnetic resonance (CMR) demonstrating focal septal late gadolinium enhancement with corresponding oedema (n = 3), occurrence of subsequent SCAD event (n = 2), or second angiogram showing healed S1-SCAD (n = 1). Patients were treated conservatively, each with ejection fraction >50%. CONCLUSION: First septal perforator SCAD events may be overlooked at initial angiography and mis-diagnosed as 'no culprit' MI. First septal perforator SCAD prevalence is likely greater than reported herein and dependent on local expertise and availability of CMR imaging. Spontaneous coronary artery dissection events may occur in intra-myocardial coronary arteries, approaching the resolution limits of invasive coronary angiography.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".