In Vivo Optical Coherence Tomography Detection of Repetitive Plaque Erosion Leading to Healed Plaques and Lesion Progression
Bibliographic record
Abstract
Plaque erosion is the second most common cause of acute coronary syndromes (ACS). Small studies using optical coherence tomography (OCT) have shown favorable outcomes in select patients with plaque erosion treated conservatively without stent implantation. Unlike plaque rupture, the role of plaque erosion in the formation of healed plaques and subsequent flow-limiting coronary stenoses is less certain. We present the case of a medically managed anterior ST-segment elevation myocardial infarction (STEMI) in a 53-year-old man, secondary to plaque erosion in the mid-left anterior descending (LAD) artery. Repeat OCT at 2 weeks demonstrated adequate resolution of intraluminal thrombus along with plaque layering with varying optical densities and negative invasive physiological testing. This case provides unique in vivo evidence of plaque erosion healing leading to the development of further plaque layering. We hypothesize that the multilayered plaque appearance after erosion is representative of repetitive episodes of plaque instability at the same coronary location, which may eventually lead to progression of plaque and reduction of lumen. Finally, there is mounting evidence that healed plaques represent an important predictor of future adverse events, raising important questions regarding the preconceived notion that plaque erosion has a benign course when treated conservatively.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".