What Is New in Spontaneous Coronary Artery Dissection?
Bibliographic record
Abstract
Spontaneous coronary artery dissection (SCAD) is a condition that leads to tearing of the coronary vessel wall in the absence of trauma, iatrogenic injury, or atherosclerosis. SCAD is an important cause of myocardial infarction in young women, leading to significant cardiovascular morbidity and mortality. Within cohorts of women aged around 50 years on average, who experience acute coronary syndrome, the prevalence of SCAD is 22.5%- 35%. Over the past decade, SCAD research has expanded rapidly, leading to improved understanding of this condition. In this review, we provide a summary of the current body of knowledge, highlight areas of ongoing research, and identify existing knowledge gaps. Specifically, we provide a focused update on the pathogenesis of SCAD, including genetic and associated conditions, clinical presentation and diagnosis, prognosis, and short-term and long-term management. Highlighted areas include the following: insights from recent genome-wide association studies; intracoronary imaging for the diagnosis of SCAD; the role of cardiac computed tomography angiography to assess for vessel healing; revascularization strategies and challenges; cardiogenic shock in SCAD; and the increasingly recognized burden of anxiety, depression, and posttraumatic stress disorder among SCAD patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".