EFFECT OF STRESS INTENSITY FACTOR IN EVALUATION OF INSTABILITY OF ATHEROSCLEROTIC PLAQUE
Bibliographic record
Abstract
Understanding the relationship between coronary arterial blood pressure, plaque morphology and composition, and sites of fibrous cap (FC) bursting, has been the focus of many recent studies. Instability of atherosclerotic plaques, defined as the propensity for FCs to burst, has been thought to occur at places where FCs are thin and necrotic core (NC) areas are large and highly compliant. However, here we show quantitatively, using a fiber-reinforced, anisotropic and hyperelastic FE model, that FC thickness and NC size and compliance alone are limited in predicting vulnerable and high-risk plaques. We suggest that plaque instabilities primarily occur at sites of high and concentrated mechanical stresses irrespective of fibrous cap thickness or NC area and compliance. Also, limitations of imaging techniques, such as intravascular ultrasound and optical coherence tomography, for providing input into (FE) models of atherosclerosis are discussed. The proposed model can be used to predict vulnerable plaque sites and rupture risks in patients. The current study also provides a framework for future research in which three-dimensional platform and viscoelastic properties of plaque composition can be considered in time-dependent and fatigue studies.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".