Bibliographic record
Abstract
PURPOSE OF REVIEW: Spontaneous coronary artery dissection (SCAD) is an important cause of myocardial infarction (MI) in women with few or no conventional cardiovascular risk factors. Lack of awareness about this condition among healthcare providers had led to significant underdiagnosis and misdiagnosis in this relatively young patient population. RECENT FINDINGS: The current review summarizes the contemporary data on cause, management strategies and outcomes of SCAD. SUMMARY: SCAD is not as rare as previously thought, accounting for up to 4% of all acute coronary syndromes. It is frequently linked with predisposing factors, such as fibromuscular dysplasia or other vasculopathies, and is often triggered by physical or emotional stress. Due to more fragile vessel architecture, coronary angiography as the first-line diagnostic tool should be performed meticulously to avoid iatrogenic dissection. Intravascular imaging may be required if angiographic findings are uncertain. Unless patients have high-risk features such as ongoing ischemia, recurrent chest pains, left main artery dissection, ventricular arrhythmias, or hemodynamic instability, a conservative treatment strategy is favored over revascularization. Close monitoring is essential after a SCAD-event as recurrent cardiovascular events post-SCAD are frequent.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".