Clinical Presentation and Outcome of Patients With Spontaneous Coronary Artery Dissection Versus Atherosclerotic Coronary Plaque Dissection
Bibliographic record
Abstract
BACKGROUND: Atherosclerotic coronary plaque dissection (ACPD) is one cause of acute coronary syndrome (ACS) caused by underlying atherosclerosis. Spontaneous coronary artery dissection (SCAD) occurs outside the setting of atherosclerosis among young women and individuals with few or no conventional atherosclerotic risk factors, and has emerged as an important cause of ACS, and sudden death. A comparison between ACPD and SCAD has not been previously addressed in the literature. Our study will compare ACPD and SCAD. METHODS: Patients with confirmed diagnosis of SCAD and ACPD were retrospectively identified from 30 centers in 4 Arab Gulf countries between January 2011 and December 2017. In-hospital (ventricular tachycardia/ventricular fibrillation, myocardial infarction (MI), percutaneous coronary intervention, dissection extension, cardiogenic shock, death, implantable cardioverter-defibrillator placement) and follow-up (MI, de novo SCAD, spontaneous superior mesenteric artery dissection, death) events were compared between them. RESULTS: Eighty-three cases of SCAD and 48 ACPD were compared. ACPD patients were more frequently male (91.67% vs. 49.40%, P < 0.001) and older (58.5 vs. 44, P < 0.001). Cardiovascular risk factors were more prevalent in patients with ACPD, including diabetes mellitus (60.4% vs. 25.3%), dyslipidemia (62.5% vs. 38.5%), and hypertension (62.5% vs. 31.3%), P < 0.001. Hospital presentation of ST-elevation MI was diagnosed in 48% of SCAD versus 27% of ACPD patients (P = 0.012). SCAD patients received medical-only treatment in 40% of cases and ACPD in 21% (P = 0.042). In-hospital and follow-up events were comparable in both groups (P = 0.25). CONCLUSIONS: Despite a completely different pathophysiology of ACS between SCAD and ACPD, in-hospital and follow-up events were comparable.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".