Angiographic types of spontaneous coronary dissection and their impact on long-term outcome
Bibliographic record
Abstract
Abstract Background Spontaneous coronary artery dissection (SCAD) is an increasingly recognized cause of myocardial infarction (MI). However, it remains unclear whether and in which extent the different angiographic types of SCAD (type 1-3) impact patients´ outcome. Methods We analyzed data from all patients included in our national SCAD study. For this analysis, only patients with angiographically classified type of SCAD and complete follow-up were eligible. Patients with different types of SCAD during their index event were excluded. Cumulative rate of recurrent MI was the primary endpoint and assessed by Kaplan-Meier curves. Results Of 684 eligible patients, SCAD type 2 during index event was the most common diagnosis affecting 478 (69.8%) patients. SCAD type 1 was more common than SCAD type 3 (150 patients (22.0%) versus 56 patients (8.2%), respectively). Within the long-term follow-up of 1800 days, 57 patients (7.9%) had a recurrent MI of which 22 patients (38.6%) had recurrent SCAD. Assessment of cumulative rate of recurrent MI showed a significant gradient within 1800 days of long-term follow-up over the 3 predefined types of SCAD (p=0.04, Figure 1). Patients with type 1 SCAD during their index event had the highest rate of recurrent MI when compared to patients with type 2 and type 3 SCAD (12.0% versus 6.7% versus 5.3%, respectively). There was a statistically significant difference when comparing the cumulative rate of recurrent MI in patients with type 1 and type 2 SCAD during 1800 days of follow-up (p=0.01). Conclusion The angiographic type of SCAD had a significant impact on the cumulative rate of recurrent MI during long-term follow-up. Patients with initial type 1 SCAD had the highest event rate when compared to patients with type 2 and 3 SCAD.Cumulative event rate over 1800 days for
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".