Angiographic appearance of spontaneous coronary artery dissection with intramural hematoma proven on intracoronary imaging
Bibliographic record
Abstract
BACKGROUND: The pathognomonic appearance of multiple radiolucent lumen on angiography is used to diagnose spontaneous coronary artery dissection (SCAD). However, this finding is absent in >70% of SCAD, in which case optical coherence tomography (OCT) or intravascular ultrasound (IVUS) is useful to assess arterial wall integrity. METHODS: We report the angiographic appearance of SCAD that were proven on intracoronary imaging with OCT or IVUS. Our angiographic classification and algorithm for SCAD diagnosis was previously reported. Patients with type 1 SCAD (multiple radiolucent lumen) do not require OCT/IVUS, whereas, it was recommended for those with suspected type 2 (diffuse stenosis) or 3 (mimic atherosclerosis) SCAD. RESULTS: Twenty-two consecutive patients with non-type 1 angiographic SCAD in 25 coronary arteries (22 OCT and 4 IVUS) were studied. Mean age was 52.9 ± 9.9 years, 89.5% were women, and 16/22 (72.7%) had underlying fibromuscular dysplasia. Sixteen SCAD arteries were type 2 SCAD, and nine were type 3. All 25 SCAD arteries had intramural hematoma and intimomedial membrane separation with double lumen on OCT or IVUS. The mean visual angiographic stenosis was 74.6 ± 17.5% (range 40-100%). Dissected segments were long with mean qualitative coronary analysis (QCA) length 45.2 ± 29.2 mm, especially in patients with type 2 SCAD (mean QCA length 58.3 ± 29.0 mm). The mean QCA length in type 3 SCAD lesions was 22.1 ± 5.7 mm. CONCLUSIONS: Intracoronary imaging confirms that SCAD may appear angiographically without multiple radiolucent lumen. Angiographers should be familiar with angiographic SCAD variants to improve SCAD diagnosis, and utilize intracoronary imaging when the diagnosis is uncertain.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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, 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".