In-vivo detection of the frequency and distribution of thin-cap fibroatheroma and ruptured plaques in patients with coronary artery disease
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to assess the prevalence and to quantify the thin-cap fibroatheroma (TCFA) and ruptured plaques in patients with coronary artery disease using optical coherence tomography (OCT). BACKGROUND: TCFA lesions are the most prevalent precursors of plaque rupture, and are responsible for acute coronary syndromes (ACS). There are limited data regarding the frequency and distribution of TCFA in diseased coronary arteries. METHODS: Coronary artery OCT was performed in 78 vessels in 47 patients, with stable angina (SA) or ACS. OCT plaque characteristics were derived using criteria that had been validated earlier. TCFA was defined as rich in lipid (two or more quadrants) with thin fibrous cap (<65 μm). Comparison was made between SA and unstable angina, and culprit and nonculprit vessels. RESULTS: There was a higher incidence of TCFA and plaque rupture (65 vs. 24%, P=0.003, and 40 vs. 15%, P=0.04) in ACS patients. This was reflected in a higher lipid pool (2.66 vs. 2.26 quadrants, P=0.04) and minimum fibrous cap thickness (52 vs. 74 μm, P=0.001) in ACS patients. The mean numbers of TCFA (2.5) were similar in patients with SA and ACS. However, the maximal length of TCFA (2.63 vs. 5.54 mm, P=0.026) and plaque rupture sites (P=0.046) were higher in ACS vessels. No relationship was found between baseline characteristics and TCFA incidence and plaque rupture. We identified ACS (P=0.002), higher mean lipid pool (P=0.002), longer TCFA length (P=0.007) and higher number of TCFA (P=0.02) as predictors of plaque rupture sites. CONCLUSION: In this in-vivo study, we identified a higher incidence of longer TCFAs and plaque rupture sites associated with ACS.
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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) | 0.001 | 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".